Paper Citation Record · LEDGER

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

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 29 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

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measured 29 of 29 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 29 of 29 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:13:44.053281Z

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

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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

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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

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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

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Observation 61f6fb8d-7714-4838-8866-f04052d7e48f · inbound

The liquid-liquid phase transition of hydrogen and its critical point: Analysis from ab initio simulation and a machine-learned potential cites this paper.

The liquid-liquid phase transition of hydrogen and its critical point: Analysis from ab initio simulation and a machine-learned potential Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 2022

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Observation 65de8356-4532-43bb-b255-800fcb8567fd · inbound

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

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 2000

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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

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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

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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

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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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High-performance training and inference for deep equivariant interatomic potentials cites this paper.

High-performance training and inference for deep equivariant interatomic potentials Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 41

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Egret-1: Pretrained Neural Network Potentials for Efficient and Accurate Bioorganic Simulation cites this paper.

Egret-1: Pretrained Neural Network Potentials for Efficient and Accurate Bioorganic Simulation Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 95

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Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties cites this paper.

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 2020

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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

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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

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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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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

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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

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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

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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

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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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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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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

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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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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

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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

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