Paper Citation Record · LEDGER

Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 23 inbound Pith citation observations for arXiv:2308.14920.

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

pith.paper-citation-record.v1
2308.14920 v3

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 23 of 23 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 23 of 23 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:08:50.059910Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

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External citation measurements

30
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 96500843-f060-4adf-95a2-71b591568f26 · inbound

MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures cites this paper.

MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 88

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:13:39.681575Z

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.

Observation 4971170e-dea7-4ca7-bfd9-5898cb3b5122 · inbound

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models cites this paper.

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:42:26.319943Z

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.

Observation b4355495-05b2-4841-a37b-a87876cd0ec6 · inbound

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models cites this paper.

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-23T18:58:19.942834Z

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.

Observation 7892a354-9edd-42bc-8070-1b374097429f · inbound

The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials cites this paper.

The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 270

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Source-reported events for the cited work

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Observation 09115957-ef85-4ab6-afbf-1390cfc4efdf · inbound

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data cites this paper.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T11:37:08.976224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8b7fc89-01df-455c-84d2-25aee9963956 · inbound

Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists cites this paper.

Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:49.022613Z

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Unavailable: canonical work link unavailable.

Observation 1f8e9b9e-9147-4d92-84b1-623f6d7b4edf · inbound

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture cites this paper.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:40.710256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46e7d839-4745-4659-8fee-218413c35494 · inbound

MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials cites this paper.

MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T19:28:48.757663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8351c7d7-dfdd-4259-9397-e981eb9befe4 · inbound

Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling cites this paper.

Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T14:05:10.195023Z

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Observation 515b014b-7ce0-461f-b671-9900bee866d6 · inbound

Universal Machine Learning Potential for Systems with Reduced Dimensionality cites this paper.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T17:53:49.382700Z

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Observation a40e38c0-a43c-4590-87ba-e381410aff15 · inbound

Universal Machine Learning Potentials under Pressure cites this paper.

Universal Machine Learning Potentials under Pressure Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T16:48:24.924561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d23484b4-6f0c-468b-9738-e0d3651660ef · inbound

OpenCSP: A Deep Learning Framework for Crystal Structure Prediction from Ambient to High Pressure cites this paper.

OpenCSP: A Deep Learning Framework for Crystal Structure Prediction from Ambient to High Pressure Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:52.344840Z

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Observation 488ef32e-0e3d-4652-bd7c-3d4f98471559 · inbound

MiAD: Mirage Atom Diffusion for De Novo Crystal Generation cites this paper.

MiAD: Mirage Atom Diffusion for De Novo Crystal Generation Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-03T21:42:04.627748Z

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Observation d2c035d6-9249-4de5-a1ea-4bf7f2d90314 · inbound

AI-Driven Expansion and Application of the Alexandria Database cites this paper.

AI-Driven Expansion and Application of the Alexandria Database Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:18:39.586017Z

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.

Observation 644d477d-70c1-43bd-86b4-6255da396cc4 · inbound

Pushing the limits of unconstrained machine-learned interatomic potentials cites this paper.

Pushing the limits of unconstrained machine-learned interatomic potentials Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T08:46:39.145092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 490ec3a6-223a-47db-90d7-c867c2451ec2 · inbound

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures cites this paper.

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-03T04:29:11.350388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1dac1b83-b2f5-44c9-91a2-6d53e8ded364 · inbound

Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning cites this paper.

Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-12T01:46:14.118168Z

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.

Observation ad4c50d3-b3f4-4719-ab76-159a78405480 · inbound

JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials cites this paper.

JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T23:52:52.655966Z

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.

Observation bd171823-7a24-4ab3-98e4-c362ca98e7be · inbound

JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials cites this paper.

JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T08:38:09.070694Z

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.

Observation c29fdfff-d78c-4b1b-917d-14b846c2439d · inbound

MatFormBench: A Benchmarking Evaluation Framework for Target-Driven Materials Formulation cites this paper.

MatFormBench: A Benchmarking Evaluation Framework for Target-Driven Materials Formulation Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-06-29T17:23:44.553342Z

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.

Observation e76ffbc1-6740-4686-98d2-34f6694e936a · inbound

DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution cites this paper.

DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 266

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T01:26:24.106015Z

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.

Observation c06403e2-58ad-43f6-a235-48198120bbdb · inbound

MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science cites this paper.

MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T19:17:18.650504Z

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.

Observation 1544cc42-fbff-4665-b3d6-b5de67e4fe2a · inbound

Toward Exascale AI for Science: A Scalable AI Skill for Autonomous Microkinetics Discovery cites this paper.

Toward Exascale AI for Science: A Scalable AI Skill for Autonomous Microkinetics Discovery Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-12T11:08:03.688246Z

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Unavailable: canonical work link unavailable.