Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation | Paper Citation Record

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This paper cites Machine learning interatomic poten- tials: library for efficient training, model development and simulation of molecular systems.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Machine learning interatomic poten- tials: library for efficient training, model development and simulation of molecular systems

Reference 1

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Observation 693f18d3-72f8-49c1-9725-6e0d5fd478d6 · outbound

This paper cites Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems

Reference 2

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Observation 8d25557e-5958-4ab9-821f-8b7e6cd6d448 · outbound

This paper cites Schoenholz and Ekin D.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Schoenholz and Ekin D

Reference 3

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Observation 85ddb15e-34d9-4401-902a-68e6b02c605c · outbound

This paper cites JAX, M.D.: A Framework for Differentiable Physics.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation JAX, M.D.: A Framework for Differentiable Physics

Reference 4

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Observation 4778ed89-081d-4087-8ff6-44ce819a1657 · outbound

This paper cites Diversity-driven training of machine- learned force fields.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Diversity-driven training of machine- learned force fields

Reference 5

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Observation 948c60bb-135f-46b4-8cbc-0dd75022729f · outbound

This paper cites Enhancing non-local interaction modeling for ab initio biomolecular calculations and simulations with visnet-pima.bioRxiv.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Enhancing non-local interaction modeling for ab initio biomolecular calculations and simulations with visnet-pima.bioRxiv

Reference 6

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Observation 86829b60-a9a0-46cd-b394-2296a4499ff0 · outbound

This paper cites Skillpuzzler: A self-evolving agentic framework for materials and chemistry research with minimal reliance on predefined tools.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Skillpuzzler: A self-evolving agentic framework for materials and chemistry research with minimal reliance on predefined tools

Reference 7

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Observation 62d13e21-7b3a-4f34-8e62-ef5cd726f6bf · outbound

This paper cites Mlipaudit: A benchmarking tool for machine learned interatomic potentials.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Mlipaudit: A benchmarking tool for machine learned interatomic potentials

Reference 8

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Observation bda74f6c-8931-48a9-b038-fcdadecf9da7 · outbound

This paper cites Lawrence Zitnick.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Lawrence Zitnick

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.820276Z

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Observation d1382f3d-0ae6-4145-a63d-289fe9380c16 · outbound

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

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-22T03:34:34.291904Z

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Observation af6b8866-aef2-4a33-94ab-61d4333c04fd · outbound

This paper cites Wood, Misko Dzamba, Xiang Fu, Meng Gao, Muhammed Shuaibi, Luis Barroso- Luque, Kareem Abdelmaqsoud, Vahe Gharakhanyan, John R.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Wood, Misko Dzamba, Xiang Fu, Meng Gao, Muhammed Shuaibi, Luis Barroso- Luque, Kareem Abdelmaqsoud, Vahe Gharakhanyan, John R

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.850502Z

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Observation b3149812-509e-4299-a43c-d34dcf118b99 · outbound

This paper cites Uma: A family of universal models for atoms.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Uma: A family of universal models for atoms

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-22T03:34:34.219910Z

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Observation bb9ca6a9-8ae6-4ff5-8f6c-82be008b85a6 · outbound

This paper cites Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E

Reference 13

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Observation 2f5c0ee1-b560-4f1d-a97a-0bca694ee1b3 · outbound

This paper cites Does equivariance matter at scale?.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Does equivariance matter at scale?

Reference 14

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Observation d0e54f41-c8b6-438f-a7ae-b909a4aad994 · outbound

This paper cites Sch¨ utt, Huziel E.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Sch¨ utt, Huziel E

Reference 15

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Observation abfd8579-3e7e-47cb-b29d-3c6d76d81e85 · outbound

This paper cites Schütt, Pan Kessel, Michael Gastegger, Kim A.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Schütt, Pan Kessel, Michael Gastegger, Kim A

Reference 16

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Observation f6310a32-34ad-4448-8b73-526f946b572e · outbound

This paper cites BeyondBOLSIG+:MonteCarlosimulation of electron and ion swarms to obtain transport and rate coefficients forplasmamodeling.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation BeyondBOLSIG+:MonteCarlosimulation of electron and ion swarms to obtain transport and rate coefficients forplasmamodeling

Reference 17

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Observation 939f188b-bdc8-4296-ae17-39e4a9a982fb · outbound

This paper cites Anstine, Roman Zubatyuk, and Olexandr Isayev.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Anstine, Roman Zubatyuk, and Olexandr Isayev

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.843720Z

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Observation 1c6859e2-03d5-42f6-8ab8-0b464e15831f · outbound

This paper cites Grossman.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Grossman

Reference 20

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Observation 55de0ae2-aac3-4eb3-a58b-7ced6c1026f1 · outbound

This paper cites doi: 10.1103/physrevlett.120.145301.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation doi: 10.1103/physrevlett.120.145301

Reference 21

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Observation 2b6e363f-5a40-4817-9550-5bf3d5cd2b94 · outbound

This paper cites E(n) Equivariant Graph Neural Networks.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation E(n) Equivariant Graph Neural Networks

Reference 22

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Observation fcc30986-576e-4801-8537-15431d5cbfac · outbound

This paper cites Directional message passing for molecular graphs.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Directional message passing for molecular graphs

Reference 23

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Observation 65a1f024-fa90-45ba-94cc-31d7649e0eb1 · outbound

This paper cites Margraf, and Stephan Günnemann.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Margraf, and Stephan Günnemann

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.790773Z

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Observation 222c261b-433f-47d3-8e5f-0840d9d11fcb · outbound

This paper cites Gemnet: Univer- sal directional graph neural networks for molecules.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Gemnet: Univer- sal directional graph neural networks for molecules

Reference 25

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Observation af43cdcb-7156-4e57-abee-0e6810074fb9 · outbound

This paper cites GemNet: Universal Directional Graph Neural Networks for Molecules.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation GemNet: Universal Directional Graph Neural Networks for Molecules

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-22T03:34:34.254310Z

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Observation 2f78ff57-5e31-477f-850c-1e7bad23e28d · outbound

This paper cites Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing.Nature Communications, 15(1), January 2024.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing.Nature Communications, 15(1), January 2024

Reference 27

Resolution: verified exact; doi, observed 2026-05-22T03:34:33.599359Z

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Observation d24ff79b-a421-4dbf-b6c9-86a842aa6e28 · outbound

This paper cites Ab initio characterization of protein molecular dynamics with ai2bmd.Nature, 635(8040):1019–1027, November 2024.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Ab initio characterization of protein molecular dynamics with ai2bmd.Nature, 635(8040):1019–1027, November 2024

Reference 28

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correction dated 2025-01-03. Source: crossref record 10.1038/s41586-024-08556-w->10.1038/s41586-024-08127-z:correction, observed 2026-07-11T03:07:51.039941+00:00. This notice travels one citation hop only.

Observation 5de8994f-7004-4e5f-8941-4c855ebfbed5 · outbound

This paper cites 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data

Reference 29

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Observation 888a59e4-bd96-4e8f-bd8a-260516134801 · outbound

This paper cites Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network

Reference 30

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Observation aded4e31-7c6c-4e02-8ede-7f4b82d96337 · outbound

This paper cites The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-22T03:34:34.239452Z

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Observation e2f1c634-11a5-4c52-a516-edb4c66d6ac9 · outbound

This paper cites Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

Reference 32

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Observation 5926e3fd-e0ef-420c-a278-034f1633622c · outbound

This paper cites MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

Reference 33

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Observation 1aba3d4a-4637-4599-9643-6ba0b737b4e3 · outbound

This paper cites Harry Moore, Nicholas J.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Harry Moore, Nicholas J

Reference 34

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Observation fa92acf1-1e77-4521-8536-2ff73ef5b1e2 · outbound

This paper cites Elena, Dávid P.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Elena, Dávid P

Reference 35

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Observation 54a23a91-e3d6-4402-8ee9-06d759d5a3d2 · outbound

This paper cites A foundation model for atomistic materials chemistry.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation A foundation model for atomistic materials chemistry

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-05-22T03:34:34.223557Z

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Observation 03df2535-2b89-40bc-8793-24aa6644ffca · outbound

This paper cites Evaluation of the mace force field architecture: From medicinal chemistry to materials science.The Journal of Chemical Physics, 159(4), July 2023.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Evaluation of the mace force field architecture: From medicinal chemistry to materials science.The Journal of Chemical Physics, 159(4), July 2023

Reference 37

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Observation d0a1dc1d-3b1b-4a31-9a66-458f22a4bde0 · outbound

This paper cites Baldwin, Domantas Kuryla, Joseph Hart, Elliott Kasoar, Alin M.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Baldwin, Domantas Kuryla, Joseph Hart, Elliott Kasoar, Alin M

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-22T03:34:34.217774Z

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Observation 27ccec7d-e885-41ce-a1af-29be03b93f54 · outbound

This paper cites Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-22T03:34:34.230735Z

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Observation 61db6fa1-29c9-4821-9a95-37e28e8a6cab · outbound

This paper cites The price of freedom: Exploring expressivity and runtime tradeoffs in equivariant tensor products.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation The price of freedom: Exploring expressivity and runtime tradeoffs in equivariant tensor products

Reference 40

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Observation 7bb20e0a-9dfb-4e74-bf32-aba023c1618e · outbound

This paper cites Asymptotically fast clebsch-gordan tensor products with vector spherical harmonics.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Asymptotically fast clebsch-gordan tensor products with vector spherical harmonics

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.776229Z

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Observation df97f4b0-e990-46be-aeb2-59109db16a72 · outbound

This paper cites Integral Formulas for Vector Signal Tensor Products.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Integral Formulas for Vector Signal Tensor Products

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-06-09T03:07:13.076301Z

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Observation cd7bb013-ff28-47ab-98d7-d44ad9d05cb8 · outbound

This paper cites Equiformer: Equivariant graph attention transformer for 3d atomistic graphs.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Equiformer: Equivariant graph attention transformer for 3d atomistic graphs

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.779822Z

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Observation 4a254196-53ca-4e14-92ee-f6eb6fc9b180 · outbound

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

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.816342Z

Source-reported events for the cited work

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Observation d5e0d17d-a521-42f2-8833-d6bd3e66cfce · outbound

This paper cites E2former: An efficient and equivariant transformer with linear-scaling tensor products.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation E2former: An efficient and equivariant transformer with linear-scaling tensor products

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.807925Z

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Observation a7e107d6-8180-4330-bb35-67a35fefcf11 · outbound

This paper cites E2Former-V2: On-the-Fly Equivariant Attention with Linear Activation Memory.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation E2Former-V2: On-the-Fly Equivariant Attention with Linear Activation Memory

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-06-08T02:03:50.596834Z

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Observation 983bf49b-2014-47f7-8c62-e4e477d94ae5 · outbound

This paper cites Wood, Aditi S.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Wood, Aditi S

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.765240Z

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Observation a6a0c0de-19a1-4c79-946f-9cfe48df6c11 · outbound

This paper cites A recipe for scalable attention- based mlips: unlocking long-range accuracy with all-to-all node attention.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation A recipe for scalable attention- based mlips: unlocking long-range accuracy with all-to-all node attention

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-22T03:34:34.225861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0cb2c005-583e-45e9-ac83-c561aeeb6953 · outbound

This paper cites Elhag, Arun Raja, Alex Morehead, Samuel M.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Elhag, Arun Raja, Alex Morehead, Samuel M

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-22T03:34:34.246817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a5fec0f1-2455-4e20-8784-63b13c1600bf · outbound

This paper cites Jraph: A library for graph neural networks in jax.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Jraph: A library for graph neural networks in jax

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.757988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ba36ee19-e130-45b8-948f-67d399db4ac4 · outbound

This paper cites Unke and Markus Meuwly.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Unke and Markus Meuwly

Reference 51

Resolution: verified exact; doi, observed 2026-05-22T03:34:33.619362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 43e314f3-9beb-4b5a-a656-fb889b1b79f7 · outbound

This paper cites Machine learning of force fields towards molecular dynamics simulations of proteins at DFT accuracy.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Machine learning of force fields towards molecular dynamics simulations of proteins at DFT accuracy

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.828645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f4cd79e7-5979-49d5-806b-700edfc13762 · outbound

This paper cites Aimnet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs.Chemical Science, 16:10228.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Aimnet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs.Chemical Science, 16:10228

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.824587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d212864c-d9a0-4fb6-ac26-ab75527fe5c0 · outbound

This paper cites Anstine, Roman Zubatyuk, and Olexandr Isayev.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Anstine, Roman Zubatyuk, and Olexandr Isayev

Reference 54

Resolution: verified exact; doi, observed 2026-05-22T03:34:33.673430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b8fa4f06-bf33-4561-9dab-9f5d93b942a2 · outbound

This paper cites A new approach to variable metric algorithms.The Computer Journal, 13(3): 317–322.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation A new approach to variable metric algorithms.The Computer Journal, 13(3): 317–322

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.753269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ca30b28c-8a4f-4e31-960b-0ab450bfa04e · outbound

This paper cites A family of variable metric updates derived by variational means.Mathematics of Computation, 24(109):23–26.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation A family of variable metric updates derived by variational means.Mathematics of Computation, 24(109):23–26

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.751117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7a58623a-ad75-4230-87a6-eb1254721219 · outbound

This paper cites Conditioning of quasi-newton methods for function minimization.Mathemat- ics of Computation, 24(111):647–656.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Conditioning of quasi-newton methods for function minimization.Mathemat- ics of Computation, 24(111):647–656

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.743930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c5bc3ee4-49b4-427d-9525-a95465c13b92 · outbound

This paper cites A dimer method for finding saddle points on high dimensional potential surfaces using only first derivatives.The Journal of Chemical Physics, 111(15):7010–7022.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation A dimer method for finding saddle points on high dimensional potential surfaces using only first derivatives.The Journal of Chemical Physics, 111(15):7010–7022

Reference 58

Resolution: verified exact; doi, observed 2026-05-22T03:34:33.654311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 57d4d29a-e06f-41be-86ac-a0d530afd497 · outbound

This paper cites Does hessian data improve the performance of machine learning potentials?Journal of Chemical Theory and Computation, 21 (14):6698–6710.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Does hessian data improve the performance of machine learning potentials?Journal of Chemical Theory and Computation, 21 (14):6698–6710

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.739534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bdbae4b5-de11-48ab-bcf6-fc52db5f4745 · outbound

This paper cites Projected hessian learning: Fast curvature supervision for accurate machine- learning interatomic potentials.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Projected hessian learning: Fast curvature supervision for accurate machine- learning interatomic potentials

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-22T03:34:34.281861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 92e27e34-0a81-49a4-9bbd-7cd39fae4465 · outbound

This paper cites Shoot from the HIP: Hessian Interatomic Potentials without derivatives.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Shoot from the HIP: Hessian Interatomic Potentials without derivatives

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-06-30T02:15:35.324486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7c21f5a7-dc32-478b-82de-1558f9ac8abd · outbound

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

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

Reference 62

Resolution: malformed identifier; arxiv_id, observed 2026-05-22T03:34:34.241833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 928a328b-2792-4bd6-9521-f58732204154 · outbound

This paper cites Elena, Sam Walton Norwood, Thomas Wolf, and Gábor Csányi.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Elena, Sam Walton Norwood, Thomas Wolf, and Gábor Csányi

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-22T03:34:34.179238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 011457fa-7da5-4a65-b449-014236bd0a93 · outbound

This paper cites Improved tangent estimate in the nudged elastic band method for finding minimum energy paths and saddle points.The Journal of Chemical Physics, 113(22):9978–9985, 12 2000.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Improved tangent estimate in the nudged elastic band method for finding minimum energy paths and saddle points.The Journal of Chemical Physics, 113(22):9978–9985, 12 2000

Reference 64

Resolution: verified exact; doi, observed 2026-05-22T03:34:33.666014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 743be513-e3ab-4c6f-95ed-cae752b1dc56 · outbound

This paper cites Uberuaga, and Hannes Jónsson.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Uberuaga, and Hannes Jónsson

Reference 65

Resolution: verified exact; doi, observed 2026-05-22T03:34:33.657854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 002f7818-94a1-4b20-9ae1-fcf525382a8b · outbound

This paper cites Improved initial guess for minimum energy path calculations.The Journal of Chemical Physics, 140(21):214106, 06 2014.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Improved initial guess for minimum energy path calculations.The Journal of Chemical Physics, 140(21):214106, 06 2014

Reference 66

Resolution: verified exact; doi, observed 2026-05-22T03:34:33.606959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7aa2b0ed-7ba2-48cb-b982-891ac8fc626e · outbound

This paper cites Ferguson.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Ferguson

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.836780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8421aa42-1f71-472f-8555-acc1313e47a2 · outbound

This paper cites doi: https://doi.org/10.1016/0010-4655(95)00059-O.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation doi: https://doi.org/10.1016/0010-4655(95)00059-O

Reference 68

Resolution: verified exact; doi, observed 2026-05-22T03:34:33.677481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 34c27a3d-7cb6-4d95-ad10-60a71273f37d · outbound

This paper cites Brandsdal.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Brandsdal

Reference 69

Resolution: verified exact; doi, observed 2026-05-22T03:34:33.615832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 157ed49b-ee98-4ca5-bc3a-67c675bc1d3f · outbound

This paper cites GabrieleCorso,HannesStärk,BowenJing,ReginaBarzilay,andTommiJaakkola.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation GabrieleCorso,HannesStärk,BowenJing,ReginaBarzilay,andTommiJaakkola

Reference 70

Resolution: verified exact; doi, observed 2026-05-22T03:34:33.630350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4e2eb2aa-40df-41f7-a04b-8d01ca4ac13f · outbound

This paper cites an unresolved cited work.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-05-22T03:34:34.832887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 87d6846d-c7c8-4582-afed-3105067be7a3 · outbound

This paper cites Berendsen, J.P.M.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Berendsen, J.P.M

Reference 72

Resolution: verified exact; doi, observed 2026-05-22T03:34:33.638260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6d4a504d-78f2-4ee7-ad53-991aac200722 · outbound

This paper cites Parrinello and A.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Parrinello and A

Reference 73

Resolution: verified exact; doi, observed 2026-05-22T03:34:33.565898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation da553183-3b77-4216-9dee-a929da185bb4 · outbound

This paper cites Characterizing dependence of samples along the langevin dynamics and algorithms via contraction of ϕ-mutual information.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Characterizing dependence of samples along the langevin dynamics and algorithms via contraction of ϕ-mutual information

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.820869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 514e81e6-8e1a-4702-a5be-3d6c78f34621 · outbound

This paper cites Levine, Muhammed Shuaibi, Evan Walter Clark Spotte-Smith, Michael G.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Levine, Muhammed Shuaibi, Evan Walter Clark Spotte-Smith, Michael G

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.828886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e34a0099-80ba-43d2-9047-f003251386c1 · outbound

This paper cites EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers

Reference 76

Resolution: verified exact; local_arxiv, observed 2026-05-22T03:34:34.276158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a9875908-8faa-43df-bf0a-15a82eb46b4a · outbound

This paper cites Dotson, Raimondas Galvelis, John E.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Dotson, Raimondas Galvelis, John E

Reference 77

Resolution: verified exact; doi, observed 2026-05-22T03:34:33.641979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b0933206-246b-4c47-be59-15addcbdc611 · outbound

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

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Transition1x - a dataset for building generalizable reactive machine learning potentials.Scientific Data, 9(1):779, December 2022

Reference 78

Resolution: verified exact; doi, observed 2026-05-22T03:34:33.662205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 967075c5-fefe-4ea9-9fe9-8cf001f08ecf · outbound

This paper cites Ogunfowora, Sanjay S.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Ogunfowora, Sanjay S

Reference 79

Resolution: verified exact; doi, observed 2026-05-22T03:34:33.570332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c6c28204-3b67-4179-a2fd-95eb64831a96 · outbound

This paper cites Pyscf: the python- based simulations of chemistry framework.Wiley Interdisciplinary Reviews: Computational Molecular Science, 8(1):e1340.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Pyscf: the python- based simulations of chemistry framework.Wiley Interdisciplinary Reviews: Computational Molecular Science, 8(1):e1340

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.824970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9c916ee4-ee0d-4150-bf11-dd317a36eb9b · outbound

This paper cites Torchani: a free and open source pytorch-based deep learning implementation of the ani neural network potentials.Journal of chemical information and modeling, 60(7):3408–3415.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Torchani: a free and open source pytorch-based deep learning implementation of the ani neural network potentials.Journal of chemical information and modeling, 60(7):3408–3415

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.840296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e4cd203c-2e2f-4e6d-8289-8886a5ca74b2 · outbound

This paper cites Miller, Mirana Claire Angel, Michael A.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Miller, Mirana Claire Angel, Michael A

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T03:34:34.853850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d1c94402-766e-447f-9a3b-5b384d0ffb3a · outbound

This paper cites and Angel, Mirana Claire and Pfeiffer, Michael A.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation and Angel, Mirana Claire and Pfeiffer, Michael A

Reference 83

Resolution: verified exact; doi, observed 2026-05-22T03:34:33.614251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 094f8f95-f243-4096-903e-3b5c9746d0f6 · outbound

This paper cites Hoover NPT dynamics for systems varying in shape and size.Molecular Physics, 78(3):533–544.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Hoover NPT dynamics for systems varying in shape and size.Molecular Physics, 78(3):533–544

Reference 84

Resolution: malformed identifier; raw_fallback, observed 2026-05-22T03:34:34.798194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d68e653c-469b-4909-a01c-0cd4bc596442 · outbound

This paper cites Jorgensen, Jayaraman Chandrasekhar, Jeffry D.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Jorgensen, Jayaraman Chandrasekhar, Jeffry D

Reference 85

Resolution: verified exact; doi, observed 2026-05-22T03:34:33.574886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 84c0ff97-fb35-4a13-8235-f9793e8d30e0 · outbound

This paper cites Vega \ and\ author J.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Vega \ and\ author J

Reference 86

Resolution: verified exact; doi, observed 2026-05-22T03:34:33.610433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6bfefdf4-8e6d-4bba-bd54-f71d539d23ad · outbound

This paper cites Bernetti and G.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Bernetti and G

Reference 87

Resolution: metadata mismatch; doi, observed 2026-05-22T03:34:33.608495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c7829da8-c349-4fbf-b889-b0864d830dad · outbound

This paper cites /path/to/xyz/or/pdb/file.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation /path/to/xyz/or/pdb/file

Reference 88

Resolution: malformed identifier; raw_fallback, observed 2026-05-22T03:34:34.794683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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Reference resolution

Outbound references

Pith citing papers