Paper Citation Record · LEDGER

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials

As of 16 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 1 inbound Pith citation observation for arXiv:2505.12447.

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

pith.paper-citation-record.v1
2505.12447 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:38:46.929437Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T19:34:52.665516Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T19:35:00.873355Z

Reference resolution

44 of 44 outbound references displayed

verified exact0
verified fuzzy30
unresolved14
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d5386f8c-9cc6-428a-a401-14223d280ae5 · outbound

This paper cites Insights into the origin of life: Did it begin from HCN and H2O? ACS Central Science, 5(9):1532–1540, 2019.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Insights into the origin of life: Did it begin from HCN and H2O? ACS Central Science, 5(9):1532–1540, 2019

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:49.028856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6e8ea9cd-0281-4476-aef5-cff82d141826 · outbound

This paper cites Glucose to 5-hydroxymethylfurfural: origin of site-selectivity resolved by machine learning based reaction sampling.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Glucose to 5-hydroxymethylfurfural: origin of site-selectivity resolved by machine learning based reaction sampling

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:48.926276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation aa993fe2-23c1-4a7a-a3f7-8a76d5c57603 · outbound

This paper cites Deep reaction network exploration of glucose pyrolysis.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Deep reaction network exploration of glucose pyrolysis

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:48.795663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 16e08196-39bc-4e11-bef6-195153df29bb · outbound

This paper cites Thermally accessible prebiotic pathways for forming ribonucleic acid and protein precursors from aqueous hydrogen cyanide.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Thermally accessible prebiotic pathways for forming ribonucleic acid and protein precursors from aqueous hydrogen cyanide

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:48.782129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2ea1b4cb-1934-405d-a57d-9783ee19f0c0 · outbound

This paper cites Chemical reaction networks and opportunities for machine learning.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Chemical reaction networks and opportunities for machine learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:48.770157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 317bad22-1f38-4f15-8643-1f535d36e50f · outbound

This paper cites A human-machine interface for automatic exploration of chemical reaction networks.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials A human-machine interface for automatic exploration of chemical reaction networks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:48.758674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ebe43b2f-3b01-41ba-a520-b571ac5a0f35 · outbound

This paper cites Simultaneously improving reaction coverage and computa- tional cost in automated reaction prediction tasks.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Simultaneously improving reaction coverage and computa- tional cost in automated reaction prediction tasks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:48.690270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 95a32e4d-1844-4fc4-a78b-2c0b43a6527c · outbound

This paper cites an unresolved cited work.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:38:48.571655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 03cd47a9-8ea2-4210-a84e-a48d4581c1b8 · outbound

This paper cites autodE: automated calculation of reaction energy profiles—application to organic and organometallic reactions.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials autodE: automated calculation of reaction energy profiles—application to organic and organometallic reactions

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:48.509404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 48f281c7-844c-41fc-8dd0-05f55cbaf983 · outbound

This paper cites Optimal transport for generating transition states in chemical reactions.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Optimal transport for generating transition states in chemical reactions

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:48.497373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 69ee45f4-e0ec-4307-9dce-ecbf89975dfe · outbound

This paper cites an unresolved cited work.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:38:48.417230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 79ea3959-bb65-4609-b219-9116603b1978 · outbound

This paper cites Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:48.381718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5576ffee-f5e3-436f-8f3b-11371b06114e · outbound

This paper cites Graph to activation energy models easily reach irreducible errors but show limited transferability.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Graph to activation energy models easily reach irreducible errors but show limited transferability

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:48.368608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d8792780-688c-40ae-b9fb-b0d29aa1d07d · outbound

This paper cites Extending machine learning beyond interatomic potentials for predicting molecular properties.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Extending machine learning beyond interatomic potentials for predicting molecular properties

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:48.355021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b4866c1e-9257-425a-9c27-e9367b0da0ea · outbound

This paper cites Neural network potentials for chemistry: concepts, applications and prospects.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Neural network potentials for chemistry: concepts, applications and prospects

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:48.343204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0512d04b-e8d0-440c-a466-002d701182ed · outbound

This paper cites Deringer, Miguel A.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Deringer, Miguel A

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:48.329818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 966eaba3-6bd5-44c8-ac01-f28edbf7da67 · outbound

This paper cites Machine learning force fields: Recent advances and remaining challenges.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Machine learning force fields: Recent advances and remaining challenges

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:48.270970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 22f79d4a-f0bb-430c-a10f-4ef4f191650a · outbound

This paper cites Machine learning force fields.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Machine learning force fields

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:48.143842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b8937f2f-f752-4262-a774-30daa360a0d1 · outbound

This paper cites Taylor, Fang Liu, Adam H.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Taylor, Fang Liu, Adam H

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:48.107503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 08101a25-ac07-44e2-9c7a-1b39427086e2 · outbound

This paper cites Neural network potentials: A concise overview of methods.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Neural network potentials: A concise overview of methods

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:48.094572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 409963d2-51fc-49ff-a467-099ccab4c287 · outbound

This paper cites Machine learning interatomic potentials and long-range physics.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Machine learning interatomic potentials and long-range physics

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:48.080133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8884d08c-c5b5-498f-b475-572262500dc5 · outbound

This paper cites Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:46.516950Z

Source-reported events for the cited work

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Observation 3fdc10ef-62fa-451b-a159-35aebd92f6a1 · outbound

This paper cites MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:46.521183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79ed55a8-cb57-4174-a5b7-46384060b0ed · outbound

This paper cites an unresolved cited work.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:38:48.014152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4504eaa3-1215-47df-9135-865d59c1fb5b · outbound

This paper cites Orb: A Fast, Scalable Neural Network Potential.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Orb: A Fast, Scalable Neural Network Potential

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:46.529404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca2c5c9d-59d5-44f4-b9b5-4603731f21d7 · outbound

This paper cites NeuralNEB—neural networks can find reaction paths fast.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials NeuralNEB—neural networks can find reaction paths fast

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:47.837112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7a20b8e5-faf6-4840-aa69-3f103e6b397b · outbound

This paper cites Analytical ab initio hessian from a deep learning potential for transition state optimization.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Analytical ab initio hessian from a deep learning potential for transition state optimization

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:47.804179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 14d67a52-86a3-40f4-9dec-4743ac441355 · outbound

This paper cites Transferable machine learning interatomic potential for pd-catalyzed cross-coupling reactions.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Transferable machine learning interatomic potential for pd-catalyzed cross-coupling reactions

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:47.790899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ce6bb78c-aebb-4677-a761-548eaa5aa022 · outbound

This paper cites Harnessing machine learning to enhance transition state search with interatomic potentials and generative models.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Harnessing machine learning to enhance transition state search with interatomic potentials and generative models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:47.777857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e71046fd-b798-4d11-abd9-4b0c6d38045b · outbound

This paper cites The dark side of the forces: assessing non- conservative force models for atomistic machine learning.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials The dark side of the forces: assessing non- conservative force models for atomistic machine learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:46.631966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b07270c-61f2-4522-bba5-624638d8e754 · outbound

This paper cites AlphaNet: Scaling Up Local-frame-based Atomistic Interatomic Potential.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials AlphaNet: Scaling Up Local-frame-based Atomistic Interatomic Potential

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:46.637053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d45715e2-1409-4151-833d-ba62da7f219b · outbound

This paper cites A new perspective on building efficient and expressive 3D equivariant graph neural networks.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials A new perspective on building efficient and expressive 3D equivariant graph neural networks

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:46.642531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef27ff3e-a549-4938-890c-ca5ac267efa2 · outbound

This paper cites Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:46.647556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcdecc8c-92ff-43ee-8b17-c9db79786db5 · outbound

This paper cites Uberuaga, and Hannes Jónsson.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Uberuaga, and Hannes Jónsson

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:47.742806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f22fc4c2-dbd9-4dc7-9c04-e12831f73f1a · outbound

This paper cites M Zimmerman.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials M Zimmerman

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:47.631358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 87450dcf-03a6-4343-a94d-4dd55c1b53e5 · outbound

This paper cites Transition1x-a dataset for building generalizable reactive machine learning potentials.Scientific Data, 9(1):779, 2022.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Transition1x-a dataset for building generalizable reactive machine learning potentials.Scientific Data, 9(1):779, 2022

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:47.616825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 444c2ec1-4cfd-4d36-abdb-c58098f26046 · outbound

This paper cites Comprehensive exploration of graphically defined reaction spaces.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Comprehensive exploration of graphically defined reaction spaces

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:47.575284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cce1ded5-9d88-4b48-bef5-8737952c48f9 · outbound

This paper cites Hessian qm9: A quantum chemistry database of molecular hessians in implicit solvents.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Hessian qm9: A quantum chemistry database of molecular hessians in implicit solvents

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:47.336930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 447ab447-f658-4852-a9e6-45e892766ea4 · outbound

This paper cites Does Hessian Data Improve the Performance of Machine Learning Potentials?.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Does Hessian Data Improve the Performance of Machine Learning Potentials?

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:46.821704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bae155d0-2756-415e-aa31-f900e10f0d38 · outbound

This paper cites A deep learning model for predicting selected organic molecular spectra.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials A deep learning model for predicting selected organic molecular spectra

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:47.322094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7de4dd87-c0d6-4a92-9c2b-2b154dbdfa7f · outbound

This paper cites Envirodetanet: Pretrained e (3)-equivariant message- passing neural networks with multi-level molecular representations for organic molecule spectra prediction.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Envirodetanet: Pretrained e (3)-equivariant message- passing neural networks with multi-level molecular representations for organic molecule spectra prediction

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:47.221763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a07bcc18-7588-4ca5-b2af-8a23656a064b · outbound

This paper cites Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:46.833114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9dad0f0-add0-47f2-b95e-f3aa3f31c905 · outbound

This paper cites Enhancing GPU-acceleration in the Python-based Simulations of Chemistry Framework.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Enhancing GPU-acceleration in the Python-based Simulations of Chemistry Framework

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:46.876671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee50db49-3c8e-4622-bda8-e802f049c89f · outbound

This paper cites Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:46.929437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 587704f9-6b0a-4ecf-8db3-571fbb251122 · inbound

THEMol dataset: Torsion, Hessian, and Energy of Molecules cites this paper.

THEMol dataset: Torsion, Hessian, and Energy of Molecules HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-06-30T19:35:00.875002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.