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Paper Citation Record · LEDGER

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials

As of 22 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2507.23160.

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

pith.paper-citation-record.v1
2507.23160 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:07:48.134400Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation 439a5776-50c3-48af-b2f8-f05d89b0e0a0 · outbound

This paper cites Machine learning in materials informatics: re- cent applications and prospects.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Machine learning in materials informatics: re- cent applications and prospects

Reference 1

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Observation 3310a194-2cab-4e1d-9d46-4da75e63bc02 · outbound

This paper cites A Review of the Progress of Thin- Film Transistors and Their Technologies for Flexible Electronics.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials A Review of the Progress of Thin- Film Transistors and Their Technologies for Flexible Electronics

Reference 2

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Observation 270050db-d868-4b20-b5cf-b69bd1caba72 · outbound

This paper cites Ultrabright and stable top- emitting quantum-dot light-emitting diodes with negligible angular color shift.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Ultrabright and stable top- emitting quantum-dot light-emitting diodes with negligible angular color shift

Reference 3

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Observation fc9a72d9-9ca9-4f87-8f7a-023dc5c45843 · outbound

This paper cites Achievements, challenges, and future prospects for industrialization of perovskite solar cells.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Achievements, challenges, and future prospects for industrialization of perovskite solar cells

Reference 4

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Observation ec142075-de66-41c7-887b-a48b6de0bcf6 · outbound

This paper cites From Design to Device: Challenges and Opportunities in Com- putational Discovery of p-Type Transparent Conductors.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials From Design to Device: Challenges and Opportunities in Com- putational Discovery of p-Type Transparent Conductors

Reference 5

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Observation e8de4adb-2f60-4be9-a76d-a0a9c00ad52a · outbound

This paper cites p- type electrical conduction in transparent thin films of CuAlO 2.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials p- type electrical conduction in transparent thin films of CuAlO 2

Reference 6

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Source-reported events for the cited work

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Observation 1f57de35-47d3-48a9-97ae-632dc5b86e32 · outbound

This paper cites AZO (Al:ZnO) thin films with high figure of merit as stable indium free transpar- ent conducting oxide.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials AZO (Al:ZnO) thin films with high figure of merit as stable indium free transpar- ent conducting oxide

Reference 7

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Source-reported events for the cited work

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Observation 4cfa3198-06d1-4a8d-9374-01b49b6a718a · outbound

This paper cites Crowd-sourcing materials-science chal- lenges with the NOMAD 2018 Kaggle compe- tition.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Crowd-sourcing materials-science chal- lenges with the NOMAD 2018 Kaggle compe- tition

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 42267434-1489-4171-bfbe-30e00385c442 · outbound

This paper cites Prop- erties of orthorhombic Ga2O3 alloyed with In2O3 and Al2O3.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Prop- erties of orthorhombic Ga2O3 alloyed with In2O3 and Al2O3

Reference 9

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Observation 1e82a5c2-25dc-4cf9-8fe7-3e44945b6b0a · outbound

This paper cites Projector augmented-wave method.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Projector augmented-wave method

Reference 10

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Source-reported events for the cited work

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Observation a732366c-edbf-4757-b4e6-fd1c74a95880 · outbound

This paper cites Efficient itera- tive schemes for ab initio total-energy calcula- tions using a plane-wave basis set.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Efficient itera- tive schemes for ab initio total-energy calcula- tions using a plane-wave basis set

Reference 11

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 0052458f-581e-454a-acc3-e7f9bf15636f · outbound

This paper cites Computational Screening of p- Type Transparent Conducting Oxides Using the Optical Absorption Spectra and Oxygen- Vacancy Formation Energies.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Computational Screening of p- Type Transparent Conducting Oxides Using the Optical Absorption Spectra and Oxygen- Vacancy Formation Energies

Reference 12

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Source-reported events for the cited work

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Observation 036104c9-7d38-4f8d-aa55-c78c1f5312fa · outbound

This paper cites A general-purpose machine-learning frame- work for predicting properties of inorganic ma- terials.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials A general-purpose machine-learning frame- work for predicting properties of inorganic ma- terials

Reference 13

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Observation b6f9eba3-6e04-4a74-b86c-abd5d09b58c9 · outbound

This paper cites A multi- fidelity machine learning approach to high throughput materials screening.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials A multi- fidelity machine learning approach to high throughput materials screening

Reference 14

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 246a8930-5b70-456a-9ca7-1a5489e78972 · outbound

This paper cites Identifying domains of applicability of ma- chine learning models for materials science.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Identifying domains of applicability of ma- chine learning models for materials science

Reference 15

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 2cf19bfc-8cd7-4398-800f-ba4ee8c354d0 · outbound

This paper cites Learning from data to design functional mate- rials without inversion symmetry.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Learning from data to design functional mate- rials without inversion symmetry

Reference 16

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation ed30f88a-e916-4b33-b95b-741b083d98e1 · outbound

This paper cites Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

Reference 17

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Observation f58d8350-e77c-4680-a1b3-78efb152fa3f · outbound

This paper cites Factorization Machines.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Factorization Machines

Reference 18

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Observation 068476bb-aac6-43ad-a7d2-4cf7ac6bf9f4 · outbound

This paper cites BPR: Bayesian person- alized ranking from implicit feedback.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials BPR: Bayesian person- alized ranking from implicit feedback

Reference 19

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 4ba4cb4d-7028-4fec-85d7-4f055ed8d520 · outbound

This paper cites Optimization by Simulated Annealing.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Optimization by Simulated Annealing

Reference 20

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Observation 04bce6eb-2b2d-451f-bcfc-1b9053197ef0 · outbound

This paper cites Quantum an- nealing in the transverse Ising model.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Quantum an- nealing in the transverse Ising model

Reference 21

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Observation 04149638-9e21-4c6e-a883-ed4918ef5832 · outbound

This paper cites Designing metamaterials with quantum annealing and factorization ma- chines.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Designing metamaterials with quantum annealing and factorization ma- chines

Reference 22

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Observation 10d2e08e-3d8c-4aec-a176-40852f640377 · outbound

This paper cites Quan- tum annealing-assisted lattice optimization.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Quan- tum annealing-assisted lattice optimization

Reference 23

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Observation e1d44c8c-7f5e-43c4-b54d-78551bac7286 · outbound

This paper cites A super- conducting copper oxide compound with elec- trons as the charge carriers.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials A super- conducting copper oxide compound with elec- trons as the charge carriers

Reference 24

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 16433cae-64b8-446b-9a8b-99afe8463d4e · outbound

This paper cites Transparent p-Type Conducting Oxides: De- sign and Fabrication of p-n Heterojunctions.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Transparent p-Type Conducting Oxides: De- sign and Fabrication of p-n Heterojunctions

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 7fb4553c-dcc1-40bd-bfed-5cdfba077ebd · outbound

This paper cites Transpar- ent conducting materials discovery using high- throughput computing.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Transpar- ent conducting materials discovery using high- throughput computing

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 3361d073-9e09-4d41-bda4-6278c790dfb2 · outbound

This paper cites Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Ma- terial Properties.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Ma- terial Properties

Reference 27

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation fa8c3de1-4701-4602-9b28-682e1786f37c · outbound

This paper cites Graph Networks as a Univer- sal Machine Learning Framework for Molecules and Crystals.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Graph Networks as a Univer- sal Machine Learning Framework for Molecules and Crystals

Reference 28

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 8f867fe4-68a7-47bd-a207-5a696201fb15 · outbound

This paper cites Pre- dicting materials properties without crystal structure: deep representation learning from stoichiometry.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Pre- dicting materials properties without crystal structure: deep representation learning from stoichiometry

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 4532be9c-77e6-4df4-b773-e7f4fa50af4f · outbound

This paper cites Generative models for inverse design of inorganic solid materials.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Generative models for inverse design of inorganic solid materials

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 602265b9-5a77-406b-b090-3bb0e8957a14 · outbound

This paper cites An improved genetic algorithm for crystal structure predic- tion.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials An improved genetic algorithm for crystal structure predic- tion

Reference 31

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 0ab54c50-e335-4cef-9fca-062d283881a1 · outbound

This paper cites Crystal structure pre- diction via particle-swarm optimization.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Crystal structure pre- diction via particle-swarm optimization

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation f67d2e3f-49ce-4078-9f21-13812c960160 · outbound

This paper cites Accelerated search for materials with targeted properties by adaptive design.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Accelerated search for materials with targeted properties by adaptive design

Reference 33

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verified fuzzy
raw_fallback, observed 2026-08-06T11:07:49.777506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T11:07:46.129874Z digest=sha256:fbbee59a2acd7350f2204f254412fdce29ebfb8d4c23ac61bfa7b2896a35fb67

Observation b1f3f605-9a32-4d88-b853-29ca144b1ff2 · outbound

This paper cites Bayesian optimization of chemical composition: A comprehensive frame- work and its application to RFe12-type magnet compounds.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Bayesian optimization of chemical composition: A comprehensive frame- work and its application to RFe12-type magnet compounds

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:07:49.768485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T11:07:46.240325Z digest=sha256:29f1227547c7c274a51b9174aebc61222e596425fcd7cf1b19e4973c68b5e29c

Observation ae538a54-9236-4c86-ac98-c834f05ab41a · outbound

This paper cites Ultranarrow-Band Wavelength-Selective Thermal Emission with Aperiodic Multilayered Metamaterials De- signed by Bayesian Optimization.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Ultranarrow-Band Wavelength-Selective Thermal Emission with Aperiodic Multilayered Metamaterials De- signed by Bayesian Optimization

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:07:49.759282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T11:07:46.388034Z digest=sha256:98db7c9b520902658fb393a9170caacfd6ef89fb31b6f11c3746e9dddbe282cd

Observation 8f5e4f8e-06cf-4528-aadb-f970c46786ef · outbound

This paper cites Deep Reinforcement Learning for Inverse Inorganic Materials Design.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Deep Reinforcement Learning for Inverse Inorganic Materials Design

Reference 36

Resolution
verified exact
local_arxiv, observed 2026-08-06T11:07:48.661650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T11:07:46.535523Z digest=sha256:ce0d1402384aba95aedfd24720b00f61a79ba9ce7b4fbcccbb037ccbbe2e8cee

Observation a2b02ec4-5014-4c0b-90b2-905279130dcc · outbound

This paper cites Reinforcement learn- ing in crystal structure prediction.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Reinforcement learn- ing in crystal structure prediction

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:07:49.750189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T11:07:46.726427Z digest=sha256:dd78e4a932bcb1b76cda2a7b91d8594d33c416d95ccb8f0a6bdb15ab6be3fb37

Observation 927835f9-2c49-42aa-a924-aa3849b25117 · outbound

This paper cites Learning con- ditional policies for crystal design using of- fline reinforcement learning.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Learning con- ditional policies for crystal design using of- fline reinforcement learning

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:07:49.740629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T11:07:46.927429Z digest=sha256:bffd013b23aafe65f99e0b5c03f9c3fc3495e0618d3e3a4f9914bb9471fc68e6

Observation b0ed8f0a-7c9f-4511-9091-33409f598537 · outbound

This paper cites Deep reinforcement learning for inverse inor- ganic materials design.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Deep reinforcement learning for inverse inor- ganic materials design

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:07:49.731194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T11:07:47.104265Z digest=sha256:d1b2d9b95bc99e09888d0f2fc216f2778dcae9884c244ae84f1ef80485293ae9

Observation 69c02aea-8a1b-4602-ad50-771b87b22e74 · outbound

This paper cites High-Performance Trans- parent Radiative Cooler Designed by Quantum Computing.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials High-Performance Trans- parent Radiative Cooler Designed by Quantum Computing

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:07:49.720266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T11:07:47.220578Z digest=sha256:9edd341b28293bb366e7adeda4b613907be34fc9b5c1e20a211e5fecfb4acc3e

Observation 1da220ad-0e8f-463e-b9ba-00aa137abafb · outbound

This paper cites Quan- tum annealing-aided design of an ultrathin- metamaterial optical diode.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Quan- tum annealing-aided design of an ultrathin- metamaterial optical diode

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:07:49.557129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T11:07:47.381051Z digest=sha256:aa3e1058a73568daa742b9fb565c73c46dbeb7a8ac7fd9f84a0192c1267815ac

Observation 61b5bf70-cb30-45a8-9301-c392e103e1c9 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Adam: A Method for Stochastic Optimization

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-06T11:07:47.534867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:07:47.534867Z digest=sha256:7ec4f1e3b49a65ec0125e00910fb690bf622752b208e03b43f85ba74ba638bda

Observation 54745c0c-6d65-4ea5-a830-9137c4c6f85a · outbound

This paper cites Multiplier and gradient meth- ods.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Multiplier and gradient meth- ods

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:07:49.381536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T11:07:47.615670Z digest=sha256:ebe8c2c52fbc2f0989c714049e26b5bb6f4d08fd4ba00db1e75eaf23272a44b0

Observation dcbb1b5c-b86f-44ca-8819-52114a2a2afd · outbound

This paper cites Neural networks and physi- cal systems with emergent collective computa- tional abilities.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Neural networks and physi- cal systems with emergent collective computa- tional abilities

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:07:49.153988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T11:07:47.744653Z digest=sha256:c2442215aef0d17f369ef361bc3d3637e5000b7c30746b0f0ec490b60d264d08

Observation 0d0bface-a071-4bd1-a718-a690a5a06575 · outbound

This paper cites Function Smoothing Regularization for Precision Factorization Machine Annealing in Continuous Variable Optimization Problems.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Function Smoothing Regularization for Precision Factorization Machine Annealing in Continuous Variable Optimization Problems

Reference 45

Resolution
verified exact
local_arxiv, observed 2026-08-06T11:07:48.363962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T11:07:47.980921Z digest=sha256:80853d877ba2d407ad95ba03a0a3c863709ad09ee2020630bc359d9f1e582fdc

Observation f6a88ca2-b573-448b-a5a1-808d1ac1a3c8 · outbound

This paper cites Optuna: A Next- generation Hyperparameter Optimization Framework.

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials Optuna: A Next- generation Hyperparameter Optimization Framework

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:07:48.914225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T11:07:48.134400Z digest=sha256:6aa5a9c205fd3f6ed0049da52b21481e2545b98a85ddbab11ff2d9eabc0310f6

Pith citing papers

No inbound Pith citation observations are available.