Paper Citation Record · LEDGER

Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 24 inbound Pith citation observations for arXiv:2210.07237.

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

pith.paper-citation-record.v1
2210.07237 v2

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 24 of 24 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:08:29.838855Z

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

163
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 8728986e-6aec-4c34-b0a3-19b8b429284a · 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 Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:13:39.676230Z

Source-reported events for the cited work

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Observation 82275f30-9ae0-41ac-a1ed-c17aeb61e6c6 · inbound

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

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:42:26.299897Z

Source-reported events for the cited work

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Observation 8edebe2e-bbff-442a-97b6-2a3d47f5a164 · inbound

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

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-23T18:58:19.977921Z

Source-reported events for the cited work

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Observation 14118850-69ee-4398-9f12-6d80be7b53e9 · inbound

NepoIP/MM: Towards Accurate Biomolecular Simulation with a Machine Learning/Molecular Mechanics Model Incorporating Polarization Effects cites this paper.

NepoIP/MM: Towards Accurate Biomolecular Simulation with a Machine Learning/Molecular Mechanics Model Incorporating Polarization Effects Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T11:08:29.838855Z

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Observation 4aa3e679-b0c6-41d7-b976-e2526cfd6aaf · inbound

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys cites this paper.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-09T04:31:33.383394Z

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Observation 897fad1e-3e96-403a-b2e2-7661a9b63713 · inbound

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials cites this paper.

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T13:51:43.137574Z

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Observation 195b10ed-9907-4296-b8ec-75807c0c0eba · inbound

Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity cites this paper.

Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 17

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Observation 92b46593-c1e6-46c7-b9a3-0e4d7a0370dc · inbound

An Iterative Framework for Generative Backmapping of Coarse Grained Proteins cites this paper.

An Iterative Framework for Generative Backmapping of Coarse Grained Proteins Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 8

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Observation 384aeeb1-ce0d-4fcd-8360-6ed2c86dbbfe · inbound

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations cites this paper.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T10:55:21.575817Z

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Observation ebc96ee8-839b-4856-8349-b240971f56ec · inbound

Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model cites this paper.

Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 2024

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Observation e68303b5-445f-434c-95b9-c6e176ae2f27 · 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 Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T04:29:08.383342Z

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Observation fef8108d-e5bd-42dd-a07a-6175d01f38e1 · inbound

Differentiable hybrid force fields support scalable autonomous electrolyte discovery cites this paper.

Differentiable hybrid force fields support scalable autonomous electrolyte discovery Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:51:38.048167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 41e6b794-78e2-42ee-add8-522d6bef327c · inbound

NEPMaker: Active learning of neuroevolution machine learning potential for large cells cites this paper.

NEPMaker: Active learning of neuroevolution machine learning potential for large cells Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:20:22.376375Z

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Observation 66144d5a-f659-4d66-9e8d-0df3233220c7 · inbound

Deep Learning for Protein Complex Prediction and Design cites this paper.

Deep Learning for Protein Complex Prediction and Design Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-13T03:57:13.022713Z

Source-reported events for the cited work

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Observation 725f7f9d-053c-462b-ac40-ff0d8d8216ee · inbound

Benchmarking empirical and machine-learned interatomic potentials using phase diagram predictions for Lead cites this paper.

Benchmarking empirical and machine-learned interatomic potentials using phase diagram predictions for Lead Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-20T17:18:46.861637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b0d84f14-68a0-4507-97c7-c8fbd7ddf163 · inbound

Harnessing AtomisticSkills for Agentic Atomistic Research cites this paper.

Harnessing AtomisticSkills for Agentic Atomistic Research Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:04:57.333440Z

Source-reported events for the cited work

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Observation 4df5e0cc-1a9d-4dc8-879d-61d154070ee6 · inbound

Dynamical properties of ab initio water from machine-learning potentials cites this paper.

Dynamical properties of ab initio water from machine-learning potentials Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-03T23:29:02.207616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ddfa8ea7-a697-4a97-8b01-bcd8061fcdb5 · inbound

Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models cites this paper.

Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-06-26T11:39:25.256899Z

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Observation 23d001b7-4cbb-4c56-a68d-626a572373fd · 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 Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:04:27.817196Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 75e9e636-67da-40b6-b458-6cc9887318fb · inbound

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

Reference 55

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Observation 135d1b8c-498b-4845-8dff-c65dd6a4353d · inbound

Geometric Algebra Meets Cartesian Tensors: Higher-Order Equivariance for Interatomic Potentials cites this paper.

Geometric Algebra Meets Cartesian Tensors: Higher-Order Equivariance for Interatomic Potentials Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-06-30T03:24:12.766579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a8e22381-027f-4c5f-8304-a085fe8c4c5d · inbound

A general-purpose atomic cluster expansion interatomic potential for niobium cites this paper.

A general-purpose atomic cluster expansion interatomic potential for niobium Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-07-02T10:36:51.800507Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9d362c15-b884-4a49-bb46-5cd5f742c3d1 · inbound

Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials cites this paper.

Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 57

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T16:28:38.319740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e3238fc8-e7ce-4c23-9161-5d22b382e414 · inbound

Girsanov Reweighting for Uncertainty Propagation in Rare-Event Kinetics cites this paper.

Girsanov Reweighting for Uncertainty Propagation in Rare-Event Kinetics Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T03:54:25.926687Z

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Unavailable: canonical work link unavailable.