Pith. sign in

Paper Citation Record · LEDGER

Periodic Topological Deep Learning for Polymer Design and Discovery

As of 8 August 2026, this Paper Citation Record lists 100 of 110 outbound references and 0 inbound Pith citation observations for arXiv:2605.26833.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2605.26833 v1

Coverage vector

measured 100 of 110 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T19:19:54.591849Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

100 of 110 outbound references displayed

  • verified exact3
  • verified fuzzy0
  • unresolved96
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 969514d5-dad2-444d-bb11-80958c385140 · outbound

This paper cites Production, use, and fate of all plastics ever made.Science advances, 3(7):e1700782, 2017.

Periodic Topological Deep Learning for Polymer Design and Discovery Production, use, and fate of all plastics ever made.Science advances, 3(7):e1700782, 2017

Reference 1

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:6a196905d013e5a1721aa55b8ffb8663acbc1c2a8e3d92d590b0698e2c7064dc

Observation f1ccc990-829f-4299-8f33-be009556c74d · outbound

This paper cites Designing polymers for advanced battery chemistries.Nature Reviews Materials, 4(5):312–330, 2019.

Periodic Topological Deep Learning for Polymer Design and Discovery Designing polymers for advanced battery chemistries.Nature Reviews Materials, 4(5):312–330, 2019

Reference 2

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:fef62fe1fc75f148cb8e78df19cab9a96bceae2201484b3fecd8ea99ada4697e

Observation bb88d1a1-8ffa-4e8b-88b5-fb891b61bd77 · outbound

This paper cites Helical polymers for biological and medical applications.Nature Reviews Chemistry, 4(6):291–310, 2020.

Periodic Topological Deep Learning for Polymer Design and Discovery Helical polymers for biological and medical applications.Nature Reviews Chemistry, 4(6):291–310, 2020

Reference 3

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:4c7f8e88ddd0e4340d51b307c8aded11578ae15c9f8e0fa4015c29462ae1cfae

Observation d8d92274-305c-460b-a76b-466f82f51178 · outbound

This paper cites Oxford University Press, 1997.

Periodic Topological Deep Learning for Polymer Design and Discovery Oxford University Press, 1997

Reference 4

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:d0ade196fd67aea73be5168f888e17b9ff004fc49c44c8c88094591b0ce898cc

Observation 99f6f279-b619-40ca-a278-ca7dbec9f236 · outbound

This paper cites Routledge, 2019.

Periodic Topological Deep Learning for Polymer Design and Discovery Routledge, 2019

Reference 5

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:5f6534b7554b3f07652275862f0deddcbbc04660a2d369888afc9d58449bd2fc

Observation aabbe21c-9836-4396-8d56-068578f8e9a9 · outbound

This paper cites Polymer history.Designed monomers and polymers, 11(1):1–15, 2008.

Periodic Topological Deep Learning for Polymer Design and Discovery Polymer history.Designed monomers and polymers, 11(1):1–15, 2008

Reference 6

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:8dd90c9203898d24f0f7b99f10e0108dbd3fcf7ac245cd5d9fd21a29cc904b47

Observation 149a0fcb-2899-4e54-bb2f-e45860fd43d3 · outbound

This paper cites Polymer informatics: opportunities and challenges.ACS macro letters, 6(10):1078– 1082, 2017.

Periodic Topological Deep Learning for Polymer Design and Discovery Polymer informatics: opportunities and challenges.ACS macro letters, 6(10):1078– 1082, 2017

Reference 7

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:4d79d91612bbfab9508ceb517801e095c6d8ddee584755299b2b618b6a169a07

Observation e985d8bd-c050-439f-82de-8c183fd12502 · outbound

This paper cites Polymer informatics: Current status and critical next steps.Materials Science and Engineering: R: Reports, 144:100595, 2021.

Periodic Topological Deep Learning for Polymer Design and Discovery Polymer informatics: Current status and critical next steps.Materials Science and Engineering: R: Reports, 144:100595, 2021

Reference 8

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:a82afbd1830db034f6dafcab44728357f0c24d452924dec376bc06b2f0955920

Observation 4af1a30d-a742-4c75-ac80-6fe3091336a9 · outbound

This paper cites Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm.Npj Computational Materials, 5(1):66, 2019.

Periodic Topological Deep Learning for Polymer Design and Discovery Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm.Npj Computational Materials, 5(1):66, 2019

Reference 9

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:592cab70af78bafe791df82aa5054464ea977aa15639be68ba9d3d901fea2f03

Observation 3249f248-f450-4e75-8bcc-f7fb8882558b · outbound

This paper cites Designing exceptional gas-separation polymer membranes using machine learning.Science advances, 6(20):eaaz4301, 2020.

Periodic Topological Deep Learning for Polymer Design and Discovery Designing exceptional gas-separation polymer membranes using machine learning.Science advances, 6(20):eaaz4301, 2020

Reference 10

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:a21bbbc70c0e6daa2bc129b659a236e945499c5cee03322b8aa66ecc65f6493c

Observation 6132174a-c902-464a-8dfb-127627787e21 · outbound

This paper cites Bioplastic design using multitask deep neural networks.Communications materials, 3(1):96, 2022.

Periodic Topological Deep Learning for Polymer Design and Discovery Bioplastic design using multitask deep neural networks.Communications materials, 3(1):96, 2022

Reference 11

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:749e25555baa4f5ff252db40f4212d57f68607305085af118ea3404d9b047d14

Observation ad570ab8-a143-4fd5-a054-409eaaa58a49 · outbound

This paper cites Extended-connectivity fingerprints.Journal of chemical information and modeling, 50(5):742–754, 2010.

Periodic Topological Deep Learning for Polymer Design and Discovery Extended-connectivity fingerprints.Journal of chemical information and modeling, 50(5):742–754, 2010

Reference 12

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:e84746c94f917ad458c9e69d889830c90eee6b8880b5cd5878cfa54c0c7993bc

Observation b23534e6-2ad2-4767-a771-b9b0bbe1eab2 · outbound

This paper cites Polymer genome: a data- powered polymer informatics platform for property predictions.The Journal of Physical Chemistry C, 122(31):17575– 17585, 2018.

Periodic Topological Deep Learning for Polymer Design and Discovery Polymer genome: a data- powered polymer informatics platform for property predictions.The Journal of Physical Chemistry C, 122(31):17575– 17585, 2018

Reference 13

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:abdcfd1b3e2494ff02d2ba2f6e7dd77f5365fc8f2078fa6c20aeac94faf5f650

Observation 8ee0abb0-4926-4b45-9ccd-17e648ec50dc · outbound

This paper cites Machine-learning predictions of polymer properties with polymer genome.Journal of Applied Physics, 128(17), 2020.

Periodic Topological Deep Learning for Polymer Design and Discovery Machine-learning predictions of polymer properties with polymer genome.Journal of Applied Physics, 128(17), 2020

Reference 14

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:62b1e1e76c9c2a165e0230579d8735b342b4206c87bef2bee1bc89bc3fa5ca0a

Observation 8f25db86-79c0-435e-85e6-453f1e03a3a7 · outbound

This paper cites Learning matter: Materials design with machine learning and atomistic simulations.Accounts of Materials Research, 3(3):343–357, 2022.

Periodic Topological Deep Learning for Polymer Design and Discovery Learning matter: Materials design with machine learning and atomistic simulations.Accounts of Materials Research, 3(3):343–357, 2022

Reference 15

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:cbb5128cdfdcddf58d8e2a4c64586beb37e62a73572d6fe2c9f089d883f04285

Observation c051409b-d8e1-452e-804c-356a4b799237 · outbound

This paper cites TransPolymer: a Transformer-based language model for polymer property predictions.npj Computational Materials, 9(1):64, 2023.

Periodic Topological Deep Learning for Polymer Design and Discovery TransPolymer: a Transformer-based language model for polymer property predictions.npj Computational Materials, 9(1):64, 2023

Reference 16

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:b25adf42ed36c35fe0f1f8d0f6092aff1646ec24889853176124e27bf4a762ab

Observation d6b29a49-f88e-42cc-84af-b356ff824270 · outbound

This paper cites polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics.Nature Communications, 14(1):4099, 2023.

Periodic Topological Deep Learning for Polymer Design and Discovery polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics.Nature Communications, 14(1):4099, 2023

Reference 17

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:06e5f9b9a9c9eda06260512e9b2e8d28ba1ddb126a68870085031d326f28684a

Observation 26abf289-a465-45a5-83d1-267293bdd6d0 · outbound

This paper cites Transformers in drug discovery: fine-tuning chemberta for high-accuracy prediction of solubility, toxicity and binding affinity.Drug Discovery Today, page 104602, 2026.

Periodic Topological Deep Learning for Polymer Design and Discovery Transformers in drug discovery: fine-tuning chemberta for high-accuracy prediction of solubility, toxicity and binding affinity.Drug Discovery Today, page 104602, 2026

Reference 18

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:ee170ee01aea6e140c783ba6b4ca1eac5988bbc9b8e5767e622b5e4b47932571

Observation ab5aaa5c-84e6-4b24-b633-bb6a8361dc0b · outbound

This paper cites Smiles, a chemical language and information system.

Periodic Topological Deep Learning for Polymer Design and Discovery Smiles, a chemical language and information system

Reference 19

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:4aa10ed34ea2e689d04259d8451c396d11e8d51314f2bd6361a1324e11ccbe22

Observation 77473bd9-fb65-4c00-bcbe-acfc9072b26a · outbound

This paper cites Graph networks as a universal machine learning framework for molecules and crystals.Chemistry of Materials, 31(9):3564–3572, 2019.

Periodic Topological Deep Learning for Polymer Design and Discovery Graph networks as a universal machine learning framework for molecules and crystals.Chemistry of Materials, 31(9):3564–3572, 2019

Reference 20

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:4448ab0902515a7de7fb135b83179a0ec32f116370f8de30b8eba2daa2e0c617

Observation 4b7a5cab-3470-4452-b5b2-0a3662a9dc31 · outbound

This paper cites Message-passing neural networks for high-throughput polymer screening.The Journal of chemical physics, 150(23), 2019.

Periodic Topological Deep Learning for Polymer Design and Discovery Message-passing neural networks for high-throughput polymer screening.The Journal of chemical physics, 150(23), 2019

Reference 21

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:10cab547e0eb9d64fc3583ecf424958a5731f74c659879027627c57fced4523d

Observation caac5305-7833-4bf2-ac52-72bd429c85aa · outbound

This paper cites Molecular contrastive learning of representations via graph neural networks.Nature Machine Intelligence, 4(3):279–287, 2022.

Periodic Topological Deep Learning for Polymer Design and Discovery Molecular contrastive learning of representations via graph neural networks.Nature Machine Intelligence, 4(3):279–287, 2022

Reference 22

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:3366d350057db2a5311d191dcc70ff8f5a7c3300186f054bbbb3961ec464a6c3

Observation 9c43d9df-516f-46da-9e33-6daba7e94229 · outbound

This paper cites Chemistry-informed macromolecule graph representa- tion for similarity computation, unsupervised and supervised learning.Machine Learning: Science and Technology, 3(1):015028, 2022.

Periodic Topological Deep Learning for Polymer Design and Discovery Chemistry-informed macromolecule graph representa- tion for similarity computation, unsupervised and supervised learning.Machine Learning: Science and Technology, 3(1):015028, 2022

Reference 23

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:c205b55fb3b30933af2df92c51da29409455a2064372a04a1b0ac1cc0bbd1bf3

Observation 619288a8-9454-4cc4-ba4a-78a36f049bf1 · outbound

This paper cites Antoniuk, Peggy Li, Bhavya Kailkhura, and Anna M.

Periodic Topological Deep Learning for Polymer Design and Discovery Antoniuk, Peggy Li, Bhavya Kailkhura, and Anna M

Reference 24

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:252f2d5f578c0973a8f0faaf959dae3e86b30ac788dd0ae600c6358408239cdf

Observation b34b1104-6ba5-4972-8f9f-4323d73d0b70 · outbound

This paper cites an unresolved cited work.

Periodic Topological Deep Learning for Polymer Design and Discovery Unresolved cited work

Reference 25

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:cf5243d2e5ce694cf9c09fa645ca04ec368e6579824ac4d681718cb9899167bf

Observation 19116945-b2fc-4ca3-96e6-41c10110eb7b · outbound

This paper cites Chemprop: a machine learning package for chemical property prediction.

Periodic Topological Deep Learning for Polymer Design and Discovery Chemprop: a machine learning package for chemical property prediction

Reference 26

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:a6bf0f3d6e644e2ede00d8679c8254a4d6a501ab52cdfd0f016e1a26c8fc0509

Observation 6f2b275e-85c6-4c80-8705-12ba8f09ae7b · outbound

This paper cites McCarver, Saitheeraj Thatigotla, Brendan P.

Periodic Topological Deep Learning for Polymer Design and Discovery McCarver, Saitheeraj Thatigotla, Brendan P

Reference 27

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:bb7785fbf4c9ca20617781e2f8a7735d1c32454e8652025f6bcdc36783586138

Observation 13da8a2f-8c5e-4db8-8c62-a752ac5e6056 · outbound

This paper cites Polymer Informatics at Scale with Multitask Graph Neural Networks.Chemistry of Materials, 35(4):1560–1567, 2023.

Periodic Topological Deep Learning for Polymer Design and Discovery Polymer Informatics at Scale with Multitask Graph Neural Networks.Chemistry of Materials, 35(4):1560–1567, 2023

Reference 28

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:9dbdd537af11bfaad5b536bd35a171b13b647a2edea40549b2ce362d57f4ba0e

Observation 182d62cf-d6db-4266-98bb-9576c3ddcb67 · outbound

This paper cites Polync: a natural and chemical language model for the prediction of unified polymer properties.Chemical Science, 15(2):534–544, 2024.

Periodic Topological Deep Learning for Polymer Design and Discovery Polync: a natural and chemical language model for the prediction of unified polymer properties.Chemical Science, 15(2):534–544, 2024

Reference 29

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:4b108b729b4f3d9b94d89e816ebd318919179d97365fb1d2a22af04a9c6c81a8

Observation d893504b-eb3a-4540-8fc0-a08ebf8fb1c1 · outbound

This paper cites MMPolymer: A Multimodal Multitask Pretraining Framework for Polymer Property Prediction.

Periodic Topological Deep Learning for Polymer Design and Discovery MMPolymer: A Multimodal Multitask Pretraining Framework for Polymer Property Prediction

Reference 30

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:bc4f8de891285132bb6bfa7b6e681f732c94ca05286f31d82fd369e53f47e93f

Observation fa427b20-06b2-4c3e-bc44-4374cbfa5ccd · outbound

This paper cites TransChem: A hybrid transformer and cheminformatics based framework for enhanced polymer informatics.Materials Today Communications, 48:113375, 2025.

Periodic Topological Deep Learning for Polymer Design and Discovery TransChem: A hybrid transformer and cheminformatics based framework for enhanced polymer informatics.Materials Today Communications, 48:113375, 2025

Reference 31

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:2f3e3870616821802675c14c7195298eadf711fa69a38589bf15bb51fa0ed7ae

Observation 31f65f3f-1d59-423c-b553-a8d142884570 · outbound

This paper cites Unified multimodal multidomain polymer representation for property prediction.npj Computational Materials, 11(1):153, 2025.

Periodic Topological Deep Learning for Polymer Design and Discovery Unified multimodal multidomain polymer representation for property prediction.npj Computational Materials, 11(1):153, 2025

Reference 32

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:a983289053df055b78405c85810de997513bf6b4a4471c957b69d748d94e64fc

Observation 3879228f-5391-4f1a-93a5-92cb6cbe1d95 · outbound

This paper cites Machine learning-assisted design of advanced polymeric materials.

Periodic Topological Deep Learning for Polymer Design and Discovery Machine learning-assisted design of advanced polymeric materials

Reference 33

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:57c8f3edc00ec71fdda8cd9cd5af2e0f648c5925b7c080250f8ee3fc43e3439d

Observation 19ece8d1-68d5-4573-b94d-6750cab84c80 · outbound

This paper cites Ai-assisted design of advanced polymeric materials: Challenges and solutions.Advanced Materials, 38(7):e16857, 2026.

Periodic Topological Deep Learning for Polymer Design and Discovery Ai-assisted design of advanced polymeric materials: Challenges and solutions.Advanced Materials, 38(7):e16857, 2026

Reference 34

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:5c63e62a9f510a1a3eb99d109f5db09f6bc46baffd2da6412c02d4ca2863a3e0

Observation 45af0be8-e197-4fe4-9d6e-e8f914cd9a21 · outbound

This paper cites Open polymer challenge: Post-competition report.arXiv preprint arXiv:2512.08896, 2025.

Periodic Topological Deep Learning for Polymer Design and Discovery Open polymer challenge: Post-competition report.arXiv preprint arXiv:2512.08896, 2025

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-06-29T19:23:53.934282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:4a15cd31b3e54bb1a2671a2519766836ddaba57827968e77d0d749c93d5c5957

Observation 17ffe682-ae53-411e-b5c8-a16f67ce3e77 · outbound

This paper cites Two-step divergent synthesis of monodisperse and ultra-long bottlebrush polymers from an easily purifiable romp monomer.Angewandte Chemie, 133(3):1552–1558, 2021.

Periodic Topological Deep Learning for Polymer Design and Discovery Two-step divergent synthesis of monodisperse and ultra-long bottlebrush polymers from an easily purifiable romp monomer.Angewandte Chemie, 133(3):1552–1558, 2021

Reference 36

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:3ccc82d69db4ef647cfae4bbf1e3466d22a131f43a31057e0f35a05e00a86ca8

Observation 592ba98c-9d43-40b3-9216-a682fd593bd1 · outbound

This paper cites Topological Deep Learning: Going Beyond Graph Data.

Periodic Topological Deep Learning for Polymer Design and Discovery Topological Deep Learning: Going Beyond Graph Data

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-06-29T19:23:53.931653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:dee516c8cf482dd1361d8308bcb1698cd78bbf3c56ae9f752908fa618b0046f3

Observation 3e41b23f-6795-4dbf-8db8-11d013d208b9 · outbound

This paper cites Combinatorial complexes: bridging the gap between cell complexes and hypergraphs.

Periodic Topological Deep Learning for Polymer Design and Discovery Combinatorial complexes: bridging the gap between cell complexes and hypergraphs

Reference 38

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:fc9d571f5f415b980b498188fb860a3e6b603e3bff08000f142b06cf5d851d1b

Observation 18092989-9079-4f02-87bf-edea27fd3e5d · outbound

This paper cites Position: Topological deep learning is the new frontier for relational learning.

Periodic Topological Deep Learning for Polymer Design and Discovery Position: Topological deep learning is the new frontier for relational learning

Reference 39

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:6325fe45da6ed3b6b98d8833afeb4de121f85666158f350fe406977f08cbe832

Observation 70de4a3f-13f3-4cc4-ab00-1dc962ae712f · outbound

This paper cites Topological data analysis in graph neural networks: Surveys and perspectives.IEEE Transactions on Neural Networks and Learning Systems, 36(6):9758–9776, 2025.

Periodic Topological Deep Learning for Polymer Design and Discovery Topological data analysis in graph neural networks: Surveys and perspectives.IEEE Transactions on Neural Networks and Learning Systems, 36(6):9758–9776, 2025

Reference 40

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:f614e81d8946d5cbc5f84bf4b8b99b3232d4a70cc7641a3c2912459ae3b0ada3

Observation 1d7bd5ac-2388-4796-8fda-fe6201c9fa8b · outbound

This paper cites Topology and data.Bulletin of the American Mathematical Society, 46(2):255–308, 2009.

Periodic Topological Deep Learning for Polymer Design and Discovery Topology and data.Bulletin of the American Mathematical Society, 46(2):255–308, 2009

Reference 41

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:063bba5bc233d832f9d34132c259a47ca2098c5c7ec71aa056dc58c0978bbb47

Observation 22421c3b-6cc3-45b2-b3ed-581b2ece8db3 · outbound

This paper cites Edelsbrunner and J.

Periodic Topological Deep Learning for Polymer Design and Discovery Edelsbrunner and J

Reference 42

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:e9bb9330db12aaf6349abed4f01df3c0f4ae9a71c53dc443416240d63c156698

Observation ccf7b2c8-c391-48c6-b94f-a9d164afe952 · outbound

This paper cites Porter, Ulrike Tillmann, Peter Grindrod, and Heather A.

Periodic Topological Deep Learning for Polymer Design and Discovery Porter, Ulrike Tillmann, Peter Grindrod, and Heather A

Reference 43

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:db0eba85c93be8cdb6ff9ec8938af1ee3fe5f0f47ee16b596baaa79a2da05f61

Observation 72a86f6e-b254-4471-b5cf-d46d7a82927e · outbound

This paper cites Bochner’s Method for Cell Complexes and Combinatorial Ricci Curvature.Discrete & Computational Geometry, 29(3):323–374, 2003.

Periodic Topological Deep Learning for Polymer Design and Discovery Bochner’s Method for Cell Complexes and Combinatorial Ricci Curvature.Discrete & Computational Geometry, 29(3):323–374, 2003

Reference 44

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:c9ca6ce7c268b032e6ba8741364eeffb3a053de5c0e3ebdb6799fba0341b7915

Observation 064f697d-f94d-4211-a136-4a5da26241c9 · outbound

This paper cites Toward a spectral theory of cellular sheaves.Journal of Applied and Computational Topology, 3(4):315–358, 2019.

Periodic Topological Deep Learning for Polymer Design and Discovery Toward a spectral theory of cellular sheaves.Journal of Applied and Computational Topology, 3(4):315–358, 2019

Reference 45

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:560c9d72ae46649cc9bc9cea130b1dd0fcc2486b04a27c1f4f4b21a6fec24b3d

Observation eff5666e-8c7c-40c1-a2d7-134224e1da0e · outbound

This paper cites What are higher-order networks?SIAM review, 65(3):686–731, 2023.

Periodic Topological Deep Learning for Polymer Design and Discovery What are higher-order networks?SIAM review, 65(3):686–731, 2023

Reference 46

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:f39f9e0a5f1b6b506526a65767a1dfb03481718f5afa1cfcd39d8e72c4230fa0

Observation f90da324-4806-4fb0-a5b4-9d1b7ce265e3 · outbound

This paper cites Montufar, Pietro Li´ o, and Michael Bronstein.

Periodic Topological Deep Learning for Polymer Design and Discovery Montufar, Pietro Li´ o, and Michael Bronstein

Reference 47

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:8b75d52c7f1e259ec3b24d28d17822d681817b89a2787e225eefa4b93e359566

Observation 51d1ad6f-c6e8-454a-bcf0-9d94ecc80191 · outbound

This paper cites Weisfeiler and lehman go cellular: Cw networks.Advances in neural information processing systems, 34:2625–2640, 2021.

Periodic Topological Deep Learning for Polymer Design and Discovery Weisfeiler and lehman go cellular: Cw networks.Advances in neural information processing systems, 34:2625–2640, 2021

Reference 48

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:ced9845b5b440cfb6ce24bbdcc6ac58333140413e56b42aa875220d6802a4a82

Observation 90c28e1d-1f79-4160-9c20-e1949319cece · outbound

This paper cites Signal processing on simplicial complexes.

Periodic Topological Deep Learning for Polymer Design and Discovery Signal processing on simplicial complexes

Reference 49

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:eb036749e7992d6e44ce234b70c2f3f65ac70c8793fde16e4f303ecbf0e75c22

Observation 0f5fac73-eca8-4480-8342-c5f608369248 · outbound

This paper cites Cell attention networks.

Periodic Topological Deep Learning for Polymer Design and Discovery Cell attention networks

Reference 50

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:9f664251c5d4e2e47e76ee6be8b695d2d83963b358668fb5582c96a9bd3081ee

Observation 65d93c6c-6148-442a-874c-2a987acc9048 · outbound

This paper cites Topologynet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions.PLoS computational biology, 13(7):e1005690, 2017.

Periodic Topological Deep Learning for Polymer Design and Discovery Topologynet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions.PLoS computational biology, 13(7):e1005690, 2017

Reference 51

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:8c77517d8a76b4f2313005d77b6f775f9cb002019604b12920c293bf1e017317

Observation 916121d3-60ee-43cb-8e15-a7a980baf93f · outbound

This paper cites A review of topological data analysis and topological deep learning in molecular sciences.

Periodic Topological Deep Learning for Polymer Design and Discovery A review of topological data analysis and topological deep learning in molecular sciences

Reference 52

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:7924e939061337d97aad086ac21dbc8347e7770edee41c33b8e92e2beebc570c

Observation d7997b0d-378f-44d6-91d2-0a05d02bdb6f · outbound

This paper cites Topological message passing for higher-order and long-range interactions.

Periodic Topological Deep Learning for Polymer Design and Discovery Topological message passing for higher-order and long-range interactions

Reference 53

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:7dc9316702f7e68f33be067ad28dddcb191d935c74d99acdd4e48337d3f36e72

Observation b6341f05-3e86-4d58-87ec-445868601f70 · outbound

This paper cites Topological graph neural networks: A novel approach for geometric deep learning.

Periodic Topological Deep Learning for Polymer Design and Discovery Topological graph neural networks: A novel approach for geometric deep learning

Reference 54

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:25fe8315d9e7a366750133f8e8d00486ae2621a33f268370ea1535ed430fdc19

Observation ccabb4ec-43f4-463d-b0e3-351c948aa42b · outbound

This paper cites Molecular topological deep learning for polymer property prediction.ACS nano, 2026.

Periodic Topological Deep Learning for Polymer Design and Discovery Molecular topological deep learning for polymer property prediction.ACS nano, 2026

Reference 55

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:bc4dada19ac8e762afa17f97fa688dcd3832760f9ab2c0d35b55c20a019b770b

Observation 2ea7a0fe-9a88-4e70-acbf-6aaac365b37e · outbound

This paper cites Self-Supervised Graph Transformer on Large-Scale Molecular Data.

Periodic Topological Deep Learning for Polymer Design and Discovery Self-Supervised Graph Transformer on Large-Scale Molecular Data

Reference 56

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:a1654d1b2e2a501e88c59b2d05af3facdd5a1e7e34910a6920dc1324cb777b3e

Observation 68f13195-e7be-4438-831c-0660b6f114d6 · outbound

This paper cites Hierarchical molecular graph self-supervised learning for property predic- tion.Communications Chemistry, 6(1):34, 2023.

Periodic Topological Deep Learning for Polymer Design and Discovery Hierarchical molecular graph self-supervised learning for property predic- tion.Communications Chemistry, 6(1):34, 2023

Reference 57

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:297758f0469084153593183c18686f394194d0f44bf4050a2b9a3a931528a783

Observation dfb6758b-1208-47cc-86b6-6db805e00727 · outbound

This paper cites Evaluating self-supervised learning for molecular graph embeddings.Advances in Neural Information Processing Systems, 36:68028–68060, 2023.

Periodic Topological Deep Learning for Polymer Design and Discovery Evaluating self-supervised learning for molecular graph embeddings.Advances in Neural Information Processing Systems, 36:68028–68060, 2023

Reference 58

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:491348a55753111edbb401578d05e15b19961b1c8c86d3924ed1dc74062135d5

Observation c7c07a08-8c29-476d-a458-cd75378f0f0a · outbound

This paper cites Motif-based graph self-supervised learning for molecular property prediction.Advances in Neural Information Processing Systems, 34:15870–15882, 2021.

Periodic Topological Deep Learning for Polymer Design and Discovery Motif-based graph self-supervised learning for molecular property prediction.Advances in Neural Information Processing Systems, 34:15870–15882, 2021

Reference 59

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:860fd98e1102bbd70262e99ba1f235f341aeb324301f963f32de85eb4664b00e

Observation e0ffbb8a-0eed-4ea9-9429-1e8a7f878a1e · outbound

This paper cites Comprehensive analysis of masking techniques in molecular graph representation learning.IEEE Access, 2025.

Periodic Topological Deep Learning for Polymer Design and Discovery Comprehensive analysis of masking techniques in molecular graph representation learning.IEEE Access, 2025

Reference 60

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:120fa2238415056bc3be3acf853cafffec3a4e7785c07659de297e5f1f5ca9da

Observation c5f26d73-f41e-44a3-bbba-02530bc2f21b · outbound

This paper cites Augmenting Polymer Datasets by Iterative Rearrangement.Journal of Chemical Information and Modeling, 63(14):4266–4276, 2023.

Periodic Topological Deep Learning for Polymer Design and Discovery Augmenting Polymer Datasets by Iterative Rearrangement.Journal of Chemical Information and Modeling, 63(14):4266–4276, 2023

Reference 61

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:f6c5049521a1dc9c4c9628eded5e1f4c0ac30171daaa0e39f4536887e3815f3a

Observation 5c27aaa5-dd12-4497-8fb9-66e3c823117e · outbound

This paper cites Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening.PLOS Computational Biology, 14(1):e1005929, 2018.

Periodic Topological Deep Learning for Polymer Design and Discovery Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening.PLOS Computational Biology, 14(1):e1005929, 2018

Reference 62

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:a15a37538e9519760082d341956175245ad0a841b3deb067092d4f8dd5c6b034

Observation 13fb3da8-f497-4ac6-8d17-6fc69e56583a · outbound

This paper cites Simplicial attention networks.

Periodic Topological Deep Learning for Polymer Design and Discovery Simplicial attention networks

Reference 63

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:10a367b4fe5f1c5f16408cb0b1e22329201e7f2a0e5ddbdbde99dafe84d97378

Observation 07dfae7a-0700-4b82-b15d-6eb52d333f87 · outbound

This paper cites Simplicial Convolutional Neural Networks.

Periodic Topological Deep Learning for Polymer Design and Discovery Simplicial Convolutional Neural Networks

Reference 64

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:13a4b2f6fc9b2a011892640df933dfd5dbbeeb2d1e0f384d5f8fea44f609f8b6

Observation ba023c98-12f9-4e39-a016-3cbc78784cf8 · outbound

This paper cites DeeperGCN: All You Need to Train Deeper GCNs, 2020.

Periodic Topological Deep Learning for Polymer Design and Discovery DeeperGCN: All You Need to Train Deeper GCNs, 2020

Reference 65

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:d6e391f0d1b4b9f58ac200f3e7422d73b05542d12592f4831519253affe09546

Observation d743c1ed-f0a2-41a0-a32b-443d37ae4d39 · outbound

This paper cites Rittig, Qinghe Gao, Manuel Dahmen, Alexander Mitsos, and Artur M.

Periodic Topological Deep Learning for Polymer Design and Discovery Rittig, Qinghe Gao, Manuel Dahmen, Alexander Mitsos, and Artur M

Reference 66

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:c812480b77524d6b476695a40c41ddc898e6d4655256e381552f993884f102ec

Observation c297f08d-0d2d-4ae8-b20f-b10305bac03d · outbound

This paper cites an unresolved cited work.

Periodic Topological Deep Learning for Polymer Design and Discovery Unresolved cited work

Reference 67

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:be4ec93c79dbed46cbd6de37aaf91e0b70072d3dac4b8e552ed23b1e95f9154a

Observation baf17054-0193-4193-a36c-65d700c75350 · outbound

This paper cites an unresolved cited work.

Periodic Topological Deep Learning for Polymer Design and Discovery Unresolved cited work

Reference 68

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:457256e83f45a17f78c5e1b9f13709f1f6ef66995c04fb3c7945c2b61b1ae795

Observation 60edfe9e-cb74-4fc1-a80a-f020fcff8a85 · outbound

This paper cites an unresolved cited work.

Periodic Topological Deep Learning for Polymer Design and Discovery Unresolved cited work

Reference 69

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:83bd4cc61b67f7d15ed3cf886d8ee00804ba4881f8a4f3b0ead3c9d010b51750

Observation 91356d2c-e2ef-48b1-99ab-93001211f4b4 · outbound

This paper cites Forman persistent ricci curvature (fprc)-based machine learning models for protein–ligand binding affinity prediction.Briefings in Bioinformatics, 22(6):bbab136, 2021.

Periodic Topological Deep Learning for Polymer Design and Discovery Forman persistent ricci curvature (fprc)-based machine learning models for protein–ligand binding affinity prediction.Briefings in Bioinformatics, 22(6):bbab136, 2021

Reference 70

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:46e3aa3b85e4bbc1897b3eb8a97d300ca654eb0a7162637bd42291a017382709

Observation 2a2675dc-8773-41e6-a358-167ad1c82b5f · outbound

This paper cites Deeper Exploiting Graph Structure Information by Discrete Ricci Curvature in a Graph Transformer.Entropy, 25(6):885, 2023.

Periodic Topological Deep Learning for Polymer Design and Discovery Deeper Exploiting Graph Structure Information by Discrete Ricci Curvature in a Graph Transformer.Entropy, 25(6):885, 2023

Reference 71

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:0ade8f4602fbfb55e886a43884f8223eb12095b30bb7162fbe91c9a3d0dace43

Observation daabf8ee-f042-4750-bdac-03efd4e6a29a · outbound

This paper cites Effective structural encodings via local curvature profiles.

Periodic Topological Deep Learning for Polymer Design and Discovery Effective structural encodings via local curvature profiles

Reference 72

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:d6688c0f3f7239bd43bf32e9dd413263ed4aba00056aca0774f588c95e033874

Observation dee797e5-6e27-4229-a8cf-b7dc0d9654be · outbound

This paper cites PI1M: A Benchmark Database for Polymer Informatics.Journal of Chemical Information and Modeling, 60(10):4684–4690, 2020.

Periodic Topological Deep Learning for Polymer Design and Discovery PI1M: A Benchmark Database for Polymer Informatics.Journal of Chemical Information and Modeling, 60(10):4684–4690, 2020

Reference 73

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:63378eb05366a69013a275f453c9c9efd4033534927375ba81bc413012593f15

Observation bbab08f9-e7b1-4374-8713-c1b653ef6334 · outbound

This paper cites Roshan Joseph, and Rampi Ramprasad.

Periodic Topological Deep Learning for Polymer Design and Discovery Roshan Joseph, and Rampi Ramprasad

Reference 74

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:0fed8e7b26c22e7b51175683715b0369b3fb6467a67466ec5b5e7ac4b3d57c39

Observation 689b2a7b-9815-4e5e-8be8-96f1e85f3309 · outbound

This paper cites Polymer informatics with multi-task learning.Patterns, 2(4):100238, 2021.

Periodic Topological Deep Learning for Polymer Design and Discovery Polymer informatics with multi-task learning.Patterns, 2(4):100238, 2021

Reference 75

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:b4a0dd7c745c436180661ed7898d0b72967af6b2db675f0f5327d49cb39d582d

Observation 71f7ea5a-c834-43d7-a903-5d647995d46d · outbound

This paper cites Estimation and Prediction of the Polymers’ Physical Characteristics Using the Machine Learning Models.Polymers, 16(1):115, 2024.

Periodic Topological Deep Learning for Polymer Design and Discovery Estimation and Prediction of the Polymers’ Physical Characteristics Using the Machine Learning Models.Polymers, 16(1):115, 2024

Reference 76

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:cfbfa310f3d9d8364161fc81c3d68b19b786acec03c8441d6766c2499d0ef565

Observation fdd8917c-7f36-4a88-beef-08cbd141e104 · outbound

This paper cites an unresolved cited work.

Periodic Topological Deep Learning for Polymer Design and Discovery Unresolved cited work

Reference 77

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:92df740773f90899d9b04d710550d022860a1514c7776946c35a8d8d522f4779

Observation 3518da5a-b2b8-47b1-8f4e-9ba0a803dc7c · outbound

This paper cites Effect of large polar side groups on the glass transition temperature of acrylic copolymers.Macromolecules, 26(14):3681–3686, 1993.

Periodic Topological Deep Learning for Polymer Design and Discovery Effect of large polar side groups on the glass transition temperature of acrylic copolymers.Macromolecules, 26(14):3681–3686, 1993

Reference 78

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:0251193977ebc5deb34ae406b79598a363c3bb4474573880cd766cd3b3e2c21f

Observation 54e3ef68-dcc3-402b-a455-09db3cc43815 · outbound

This paper cites Influence of acrylamide monomer addition to the acrylic denture-base resins on mechanical and physical properties.International Journal of Oral Science, 5(4):229–235, 2013.

Periodic Topological Deep Learning for Polymer Design and Discovery Influence of acrylamide monomer addition to the acrylic denture-base resins on mechanical and physical properties.International Journal of Oral Science, 5(4):229–235, 2013

Reference 79

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:77f34c9fce9ec7c7c0fc2f0e117fb67a4702376d05eaef8d23d1dcd6c08ad1f8

Observation 61636759-0ae1-4425-9969-1670eb4c53fe · outbound

This paper cites Haehnel, Andrea M.

Periodic Topological Deep Learning for Polymer Design and Discovery Haehnel, Andrea M

Reference 80

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:ea8f9bb4dbeb2c45ef693d069059b50fc6d2335ce8adf52d51c709d893409093

Observation 2af167f9-d36e-4aa9-8bf8-4a8b87593655 · outbound

This paper cites an unresolved cited work.

Periodic Topological Deep Learning for Polymer Design and Discovery Unresolved cited work

Reference 81

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:9484cd5dd45fdb6a88afc586526b86e9e5bb92f38b56191d79033b638a9811a1

Observation 3e711009-6216-4386-95d4-9ce3169742f7 · outbound

This paper cites High Glass-Transition Temperature Acrylate Polymers Derived from Biomasses, Syringaldehyde, and Vanillin.Macromolecular Chemistry and Physics, 217(21):2402–2408, 2016.

Periodic Topological Deep Learning for Polymer Design and Discovery High Glass-Transition Temperature Acrylate Polymers Derived from Biomasses, Syringaldehyde, and Vanillin.Macromolecular Chemistry and Physics, 217(21):2402–2408, 2016

Reference 82

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:8ae1fa0fef786bf8fa46d6102fabf8cf0105e760a0fe335586eb7bb3f2b860dc

Observation 7b6e6e1a-9daf-4977-b36c-5d907e465f74 · outbound

This paper cites Synthesis of (meth)acrylamide-based glycomonomers using renewable resources and their polymerization in aqueous systems.Green Chemistry, 20(2):476–484, 2018.

Periodic Topological Deep Learning for Polymer Design and Discovery Synthesis of (meth)acrylamide-based glycomonomers using renewable resources and their polymerization in aqueous systems.Green Chemistry, 20(2):476–484, 2018

Reference 83

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:e552877578d923d8d8247b70e853f467be9037bf2491978ee748ca5f9d7ebcd6

Observation 625b5a0b-8e15-46cb-aaeb-6a7890a49387 · outbound

This paper cites an unresolved cited work.

Periodic Topological Deep Learning for Polymer Design and Discovery Unresolved cited work

Reference 84

Resolution
malformed identifier
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:bf9e3d7c0334aacd601de5808c39ae482ca98ec6ceb12aac05b39bb4b33bf136

Observation a16e0003-cd07-455e-b5b5-07a5f33cb196 · outbound

This paper cites an unresolved cited work.

Periodic Topological Deep Learning for Polymer Design and Discovery Unresolved cited work

Reference 85

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:3209ee9c327c471e08cde325a5c8e7972330a9c6f4bbb2d955665d313f2a7dfd

Observation 60165f12-ff8b-4602-96f9-af28911de3a7 · outbound

This paper cites Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions.Journal of Cheminformatics, 1(1):8, 2009.

Periodic Topological Deep Learning for Polymer Design and Discovery Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions.Journal of Cheminformatics, 1(1):8, 2009

Reference 86

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:af43f4abb123bcb11e0c915c485a624cab2d188197fa55841fdece8c5bc0ba03

Observation 3967da3c-2ac1-4e88-9d83-813e19382315 · outbound

This paper cites Molecular geometric deep learning.Cell reports methods, 3(11), 2023.

Periodic Topological Deep Learning for Polymer Design and Discovery Molecular geometric deep learning.Cell reports methods, 3(11), 2023

Reference 87

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:804cb9224df4a13c2fd7df5e6c64ae55137aba7f78a8e5ceb872e15cc3df77bf

Observation 003a1748-e4bf-424b-966f-caf33e22933c · outbound

This paper cites Topology-aware multiscale mixture of experts for efficient molecular property prediction.arXiv preprint arXiv:2601.12637, 2026.

Periodic Topological Deep Learning for Polymer Design and Discovery Topology-aware multiscale mixture of experts for efficient molecular property prediction.arXiv preprint arXiv:2601.12637, 2026

Reference 88

Resolution
verified exact
arxiv_id, observed 2026-06-29T19:23:53.928813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:c3def6400cff5e2d8c0b3bba1b788a7b2ae306639a65343d2f67abd1b3107af7

Observation 19e8f8c2-fd00-41bc-b9b6-605e88d0da16 · outbound

This paper cites Bigsmiles: a structurally-based line notation for describing macromolecules.ACS central science, 5(9):1523–1531, 2019.

Periodic Topological Deep Learning for Polymer Design and Discovery Bigsmiles: a structurally-based line notation for describing macromolecules.ACS central science, 5(9):1523–1531, 2019

Reference 89

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:c5009585695a6fdaf8dc05437ce5d2669a6f4739de8a3d7978afe69716752163

Observation ab743703-7bf0-4932-a8fa-7c67493b2b94 · outbound

This paper cites Quotient complex (qc)-based machine learning for 2d hybrid perovskite design.Journal of Chemical Information and Modeling, 65(2):660–671, 2025.

Periodic Topological Deep Learning for Polymer Design and Discovery Quotient complex (qc)-based machine learning for 2d hybrid perovskite design.Journal of Chemical Information and Modeling, 65(2):660–671, 2025

Reference 90

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:302c4f9a393b893b10237160007d3253f5cca4974f6f85aa176292528a5274bd

Observation 780f5356-b281-474c-ada7-88c03984d85c · outbound

This paper cites Quotient-complex transformer for perovskite data analysis.Cell Reports Physical Science, 2026.

Periodic Topological Deep Learning for Polymer Design and Discovery Quotient-complex transformer for perovskite data analysis.Cell Reports Physical Science, 2026

Reference 91

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:bbfaf759a746436fa05c115304120c99f9b4997cf1d70df78a19201c61e9dc87

Observation ea3974bb-d736-45a4-99a3-89cc8bbeb85d · outbound

This paper cites Cross-linking–effect on physical properties of polymers.Journal of Macromolecular Science, Part C, 3(1):69–103, 1969.

Periodic Topological Deep Learning for Polymer Design and Discovery Cross-linking–effect on physical properties of polymers.Journal of Macromolecular Science, Part C, 3(1):69–103, 1969

Reference 92

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:0a676d1e4d97451dc898a676eee761a8a93fd4f479d8e06c1146a758afe4fc8d

Observation b734edcf-730d-4dea-97c6-ab5639a86062 · outbound

This paper cites Star polymer networks: a toolbox for cross-linked polymers with controlled structure.Polymer Chemistry, 13(15):2074–2107, 2022.

Periodic Topological Deep Learning for Polymer Design and Discovery Star polymer networks: a toolbox for cross-linked polymers with controlled structure.Polymer Chemistry, 13(15):2074–2107, 2022

Reference 93

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:4fcea3e8cb5690515522d97e1e261e0241f2be0698d1274bfd1d70fd6f0260a5

Observation 0664f929-5b08-438f-8b55-e9f7587f19aa · outbound

This paper cites an unresolved cited work.

Periodic Topological Deep Learning for Polymer Design and Discovery Unresolved cited work

Reference 94

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:99dccfb46b16fc6467b8078918e3beb7026ea4230801b7b1f0ea91a9b5cf7811

Observation 8a4d4ec0-63fc-41ca-97d9-73ae35b94542 · outbound

This paper cites an unresolved cited work.

Periodic Topological Deep Learning for Polymer Design and Discovery Unresolved cited work

Reference 95

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:2adf5a2f070929028a7209850f5bbf9d34fa7f1b61da520296576d6f8efec193

Observation f4692df0-0c72-41d5-a19c-2a696b9ca6d9 · outbound

This paper cites Decoupled Weight Decay Regularization.

Periodic Topological Deep Learning for Polymer Design and Discovery Decoupled Weight Decay Regularization

Reference 96

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:0ad398ee1a4282aab4590accf6aa0f65abe635716e638b0e79a6d8c328b4593d

Observation ac1e2036-7699-4b85-b008-26a8d6b9129c · outbound

This paper cites On the difficulty of training recurrent neural networks.

Periodic Topological Deep Learning for Polymer Design and Discovery On the difficulty of training recurrent neural networks

Reference 97

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:181680361173a63d80b08188824fc5efb902f2196abc7a6296d8c7bb32c7f8b2

Observation f80a15c5-1219-465c-88a3-8ddfd7ec6fc9 · outbound

This paper cites an unresolved cited work.

Periodic Topological Deep Learning for Polymer Design and Discovery Unresolved cited work

Reference 98

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:c1aa3313e205c8a93b1563ef1ba11050cbbcb0f8e0b655b87c293af72ed95d62

Observation bedb4cfe-92bc-42e3-8cd6-8a1e8d4fb74c · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Periodic Topological Deep Learning for Polymer Design and Discovery SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 99

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:6784ab239d7b096cd738ef6dd0e7d4332e4c928b1d2ea5c280cb6ee0a6233d0a

Observation b6c41044-9479-48db-a613-65113d29ce93 · outbound

This paper cites Early Stopping - But When? InNeural Networks: Tricks of the Trade, pages 55–69.

Periodic Topological Deep Learning for Polymer Design and Discovery Early Stopping - But When? InNeural Networks: Tricks of the Trade, pages 55–69

Reference 100

Resolution
unresolved
no resolver link, observed 2026-06-29T19:19:54.591849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:19:54.591849Z digest=sha256:908f258b8a5139af697d832766e5b2d7699206413e8a2ce2f72b96cf6388172f

Pith citing papers

No inbound Pith citation observations are available.