AtomProNet: Data flow to and from machine learning interatomic potentials in materials science | Paper Citation Record

Observation 8ed91407-cb5c-497f-af8d-7642fbabf025 · outbound

This paper cites Thirty years of density functional theory in computa- tional chemistry: an overview and extensive assessment of 200 density functionals,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Thirty years of density functional theory in computa- tional chemistry: an overview and extensive assessment of 200 density functionals,

Reference 1

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

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Machine learning interatomic potentials and long-range physics,

Reference 2

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Observation d12b95f6-76da-46b8-ab51-cb86822b30c6 · outbound

This paper cites Commentary: The Materials Project: A materials genome approach to accelerating materials innovation,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Commentary: The Materials Project: A materials genome approach to accelerating materials innovation,

Reference 3

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This paper cites Crystallography Open Database – an open-access collection of crystal structures,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Crystallography Open Database – an open-access collection of crystal structures,

Reference 4

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This paper cites Aflowlib.org: A distributed materials properties repository from high-throughput ab initio calculations,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Aflowlib.org: A distributed materials properties repository from high-throughput ab initio calculations,

Reference 5

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This paper cites Nomad: The fair concept for big data-driven materials science,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Nomad: The fair concept for big data-driven materials science,

Reference 6

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This paper cites The joint automated repository for various integrated simulations (jarvis) for data-driven materials design,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science The joint automated repository for various integrated simulations (jarvis) for data-driven materials design,

Reference 7

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This paper cites The materials commons: A collaboration platform and information repository for the global materials community,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science The materials commons: A collaboration platform and information repository for the global materials community,

Reference 8

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Observation caceaf07-d33f-44d3-9b18-489ce4e30e46 · outbound

This paper cites Improving machine-learning models in materials science through large datasets,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Improving machine-learning models in materials science through large datasets,

Reference 9

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Observation 875f6cbd-19ed-4e57-8536-d0a9d9251956 · outbound

This paper cites The open quantum materials database (oqmd): assessing the accuracy of dft formation energies,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science The open quantum materials database (oqmd): assessing the accuracy of dft formation energies,

Reference 10

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Observation 45471397-f830-4efb-8ee9-f1092b10927f · outbound

This paper cites Open materials 2024 (omat24) inorganic materials dataset and models,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Open materials 2024 (omat24) inorganic materials dataset and models,

Reference 11

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Observation fc2b27fa-92f5-4a85-93d1-8d0c41e22345 · outbound

This paper cites An open experimental database for exploring inorganic materials,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science An open experimental database for exploring inorganic materials,

Reference 12

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This paper cites The inorganic crystal structure database (icsd)—present and future,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science The inorganic crystal structure database (icsd)—present and future,

Reference 13

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Observation 5cc474ea-b3d0-4b17-949a-35a1900180d0 · outbound

This paper cites The Cambridge Structural Database,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science The Cambridge Structural Database,

Reference 14

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Observation 2a5d651d-8f2d-49d6-bb8b-aa0816a3ea35 · outbound

This paper cites Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning,

Reference 15

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This paper cites Inhomogeneous electron gas,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Inhomogeneous electron gas,

Reference 16

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Observation d05082b8-9943-4bd2-b20b-21da4f848c22 · outbound

This paper cites Machine-learning interatomic potentials for materials science,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Machine-learning interatomic potentials for materials science,

Reference 18

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Observation 4227ce2a-471b-4146-97a5-3d05524f076a · outbound

This paper cites Performance and cost assessment of machine learning interatomic potentials,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Performance and cost assessment of machine learning interatomic potentials,

Reference 19

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This paper cites Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set,

Reference 20

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This paper cites Quantum ESPRESSO toward the exascale,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Quantum ESPRESSO toward the exascale,

Reference 21

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This paper cites Fast parallel algorithms for short-range molecular dynamics,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Fast parallel algorithms for short-range molecular dynamics,

Reference 22

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Observation 803b23a6-d1c6-475b-8a54-c29f96cae088 · outbound

This paper cites The atomic simulation environment—a python library for working with atoms,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science The atomic simulation environment—a python library for working with atoms,

Reference 23

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Observation acd48f6d-03df-4233-827c-5369c08e387b · outbound

This paper cites Amp: A modular approach to machine learning in atomistic simulations,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Amp: A modular approach to machine learning in atomistic simulations,

Reference 24

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This paper cites Comp- physvienna/n2p2: Version 2.1.4,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Comp- physvienna/n2p2: Version 2.1.4,

Reference 25

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This paper cites An implementation of artificial neural-network potentials for atom- istic materials simulations: Performance for TiO2,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science An implementation of artificial neural-network potentials for atom- istic materials simulations: Performance for TiO2,

Reference 26

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Observation 8ae60084-28ee-4214-a004-a7c23f5c9000 · outbound

This paper cites Knowledgebase of interatomic models (KIM) application programming interface (API),.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Knowledgebase of interatomic models (KIM) application programming interface (API),

Reference 27

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Observation dc9e29a1-ae46-46b3-a289-f1ba5775682b · outbound

This paper cites Maise: Construction of neural network interatomic models and evolutionary structure optimization,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Maise: Construction of neural network interatomic models and evolutionary structure optimization,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.575068Z

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Observation 99a8d158-4e5f-4ddd-89da-a4f0a0f72942 · outbound

This paper cites Generalized neural-network representation of high-dimensional potential-energy surfaces,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Generalized neural-network representation of high-dimensional potential-energy surfaces,

Reference 29

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Observation 5757fb7e-736b-4fe8-8fc0-8be66b23721b · outbound

This paper cites Atom-centered symmetry functions for constructing high-dimensional neural net- work potentials,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Atom-centered symmetry functions for constructing high-dimensional neural net- work potentials,

Reference 30

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This paper cites Four generations of high-dimensional neural network potentials,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Four generations of high-dimensional neural network potentials,

Reference 31

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Observation 47eede35-f8c8-4960-8e7f-9823060ad37f · outbound

This paper cites E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials,

Reference 32

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Observation f3b06dcd-46a6-49ba-8c3a-4b75d7625838 · outbound

This paper cites e3nn: Euclidean neural networks,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science e3nn: Euclidean neural networks,

Reference 33

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This paper cites How to validate machine-learned interatomic potentials,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science How to validate machine-learned interatomic potentials,

Reference 34

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Observation 94ad6388-4f4d-4a80-96a7-69271b48b7c9 · outbound

This paper cites An accurate and transferable machine learning potential for carbon,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science An accurate and transferable machine learning potential for carbon,

Reference 35

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Observation 129d7d6e-97fe-419d-a423-83926abffbbd · outbound

This paper cites Best practices in machine learning for chemistry,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Best practices in machine learning for chemistry,

Reference 36

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Observation c0555da2-60c3-4a3a-9ed1-94dc4a751188 · outbound

This paper cites Evaluation guidelines for machine learning tools in the chemical sciences,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Evaluation guidelines for machine learning tools in the chemical sciences,

Reference 37

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Observation d7a6d662-0b24-4646-bf10-d330120dd4c2 · outbound

This paper cites Methods for compar- ing uncertainty quantifications for material property predictions,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Methods for compar- ing uncertainty quantifications for material property predictions,

Reference 38

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Observation c98a8391-b795-4365-bde1-6c73c802fc13 · outbound

This paper cites Characterizing uncertainty in machine learning for chemistry,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Characterizing uncertainty in machine learning for chemistry,

Reference 39

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Observation 49b34d00-6f69-4e93-8d61-5eb527bc6158 · outbound

This paper cites Gaus- sian process regression for materials and molecules,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Gaus- sian process regression for materials and molecules,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.425693Z

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Observation 5d69ff3b-8c18-4deb-9e06-aefed5709c9b · outbound

This paper cites Convergence acceleration in machine learning potentials for atomistic simulations,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Convergence acceleration in machine learning potentials for atomistic simulations,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.414257Z

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Observation 7629a6ce-ab93-4091-bc78-49a2d1e94f96 · outbound

This paper cites Physics-inspired structural representations for molecules and materials,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Physics-inspired structural representations for molecules and materials,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.401860Z

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Observation 107992a7-72ee-4623-b81a-d4b54546a227 · outbound

This paper cites Incompleteness of atomic structure representations,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Incompleteness of atomic structure representations,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.388178Z

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Observation e935f36c-a5b7-48f9-ac39-98d767445995 · outbound

This paper cites Choosing the right molecular machine learning potential,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Choosing the right molecular machine learning potential,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.375598Z

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Observation 2d89dab6-6e40-42ff-adb8-4e000addce0a · outbound

This paper cites Machine learning force fields,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Machine learning force fields,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.362826Z

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Observation 98b02f7e-e1a8-437c-98a3-5c148a824566 · outbound

This paper cites Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.350185Z

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Observation ed1612a1-bce8-4bd3-908e-88c3027b938b · outbound

This paper cites SchNet – A deep learning architecture for molecules and materials,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science SchNet – A deep learning architecture for molecules and materials,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.337018Z

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Observation 31d0de46-67a1-4693-a89c-5d1c4ba306f5 · outbound

This paper cites Gaussian approximation poten- tials: The accuracy of quantum mechanics, without the electrons,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Gaussian approximation poten- tials: The accuracy of quantum mechanics, without the electrons,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.324214Z

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Observation 9edbfc5d-7330-47ab-afb5-32a752bb2ef2 · outbound

This paper cites Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.311263Z

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Observation b40664a6-b553-486e-a458-b9e9f7d48e70 · outbound

This paper cites Moment tensor potentials: A class of systematically improvable interatomic potentials,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Moment tensor potentials: A class of systematically improvable interatomic potentials,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.298183Z

Source-reported events for the cited work

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Observation 61012123-9fed-4923-b334-cc1514cdf2db · outbound

This paper cites Atomic cluster expansion for accurate and transferable interatomic potentials,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Atomic cluster expansion for accurate and transferable interatomic potentials,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.285411Z

Source-reported events for the cited work

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Observation c2ebab66-23f6-486a-97d1-93e2fcba265e · outbound

This paper cites A foundation model for atomistic materials chemistry,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science A foundation model for atomistic materials chemistry,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.272313Z

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Observation 7dce6bfb-bd71-45cd-90d1-0089a4bbc1c3 · outbound

This paper cites Chgnet as a pretrained universal neural network potential for charge-informed atomistic modelling,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Chgnet as a pretrained universal neural network potential for charge-informed atomistic modelling,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.259717Z

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Observation aef9c0b9-7a0d-4f0a-bf1c-7235e86e05cd · outbound

This paper cites A universal graph deep learning interatomic potential for the periodic table,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science A universal graph deep learning interatomic potential for the periodic table,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.247204Z

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Observation 49d8dc43-b69e-4a0d-bb4d-1920aedbd9ae · outbound

This paper cites On representing chemical environments,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science On representing chemical environments,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.234874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7a877ba2-01c1-462f-b243-5cb0202b0e05 · outbound

This paper cites Fast and accurate modeling of molecular atomization energies with machine learning,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Fast and accurate modeling of molecular atomization energies with machine learning,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.222838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 60ef4ac7-2606-4f41-805f-329105d3dbe7 · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial dif- ferential equations,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial dif- ferential equations,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.210555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1f3b73a4-77b9-40b2-9c61-1e05998d6dd7 · outbound

This paper cites Neural network potential-energy surfaces in chemistry: a tool for large-scale sim- ulations,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Neural network potential-energy surfaces in chemistry: a tool for large-scale sim- ulations,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.197381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2aca4f33-6e70-4d8b-8620-5628894f754a · outbound

This paper cites Die berechnung optischer und elektrostatischer gitterpotentiale,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Die berechnung optischer und elektrostatischer gitterpotentiale,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.184820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a0a0de93-3203-4858-8eeb-7fe0fa04ba12 · outbound

This paper cites Physnet: A neural network for predicting energies, forces, dipole moments, and partial charges,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Physnet: A neural network for predicting energies, forces, dipole moments, and partial charges,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.171988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5d528f76-5bf6-437a-875e-6933a99d611d · outbound

This paper cites The tensormol-0.1 model chem- istry: a neural network augmented with long-range physics,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science The tensormol-0.1 model chem- istry: a neural network augmented with long-range physics,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.159143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 904aaab4-18bb-498c-a7bf-551b92ae2c14 · outbound

This paper cites A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H- Pu,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H- Pu,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.146202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 89fa4b09-07c8-40bf-88d4-426ab1d4d005 · outbound

This paper cites Electronic Population Analysis on LCAO–MO Molecular Wave Functions. I,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Electronic Population Analysis on LCAO–MO Molecular Wave Functions. I,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.132961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c4e85ee0-6a06-4488-857c-0d403cbde438 · outbound

This paper cites Atoms in molecules,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Atoms in molecules,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.119879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b9fee167-3916-47c5-9118-5da99fba9c6f · outbound

This paper cites Reaxff: A reactive force field for hydrocarbons,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Reaxff: A reactive force field for hydrocarbons,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.106874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5b47faa1-453f-451a-a73c-a934c87b7cec · outbound

This paper cites Electronegativity-equalization method for the calculation of atomic charges in molecules,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Electronegativity-equalization method for the calculation of atomic charges in molecules,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.093578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c629392a-1353-44bf-8e03-80b7fe39a7db · outbound

This paper cites Charge equilibration for molecular dynamics simula- tions,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Charge equilibration for molecular dynamics simula- tions,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.080262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ccbbe9a2-4738-4d27-9aeb-aee60612121e · outbound

This paper cites Learning representations by back- propagating errors,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Learning representations by back- propagating errors,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.066916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 80639f1a-212d-4d11-8bd8-dd6d4ca8ff8f · outbound

This paper cites Self-consistent equations including exchange and correlation ef- fects,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Self-consistent equations including exchange and correlation ef- fects,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-10T15:31:51.436905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2038fcc-c490-486f-b6e3-f8c00882ac55 · outbound

This paper cites Inhomogeneous electron gas,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Inhomogeneous electron gas,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.053048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 77bfd3ff-14f9-4329-8d4e-bf282f0a8188 · outbound

This paper cites Generalized gradient approximation made sim- ple,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Generalized gradient approximation made sim- ple,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.038917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4fef32b8-8617-42d5-8c94-b0e7634ec7d8 · outbound

This paper cites From ultrasoft pseudopotentials to the projector augmented-wave method,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science From ultrasoft pseudopotentials to the projector augmented-wave method,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.022835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 22680050-7343-430a-9226-85a2bdb856f5 · outbound

This paper cites Engineering chemo-mechanical properties of zn surfaces via alucone coating,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Engineering chemo-mechanical properties of zn surfaces via alucone coating,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:52.008247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f85ecffc-5d54-4c12-a516-6dadb9994412 · outbound

This paper cites Matbench discovery – a framework to evaluate machine learning crystal stability predictions,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Matbench discovery – a framework to evaluate machine learning crystal stability predictions,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.993967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 38e8aebf-65a5-4590-9cb2-b1898e719443 · outbound

This paper cites Mace: Higher order equivariant message passing neural networks for fast and accurate force fields,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Mace: Higher order equivariant message passing neural networks for fast and accurate force fields,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.979709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8ae179f6-307b-4b9e-be41-df5a04445e70 · outbound

This paper cites Learning local equivariant representations for large-scale atomistic dynamics,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Learning local equivariant representations for large-scale atomistic dynamics,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.966065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 52c21111-715b-4387-8ae3-faa2d55f8f92 · outbound

This paper cites Gaussian error linear units (gelus),.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Gaussian error linear units (gelus),

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-10T15:31:51.471692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b68281de-7154-4232-8f77-b38af69fe47e · outbound

This paper cites Adam: A method for stochastic optimization,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Adam: A method for stochastic optimization,

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-10T15:31:51.475387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bd242c5-bed3-4ab8-a2ad-55f3ecb0b490 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Pytorch: An imperative style, high-performance deep learning library,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.932820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ec96b583-41b7-4bf4-beda-5459be21412c · outbound

This paper cites Computer “experiments.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Computer “experiments

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.918655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bbca582d-52ed-4f29-afb6-73967aaa3575 · outbound

This paper cites Interatomic potentials: achievements and chal- lenges,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Interatomic potentials: achievements and chal- lenges,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.903258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d3f74a41-7560-453d-8b46-6bc077cc06cb · outbound

This paper cites Review of force fields and intermolecular potentials used in atomistic computational materials research,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Review of force fields and intermolecular potentials used in atomistic computational materials research,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.890109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d9acd24b-1ad5-43a1-9280-8f51dbcbd103 · outbound

This paper cites Classical and reactive molecular dynamics: Principles and applications in combustion and energy systems,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Classical and reactive molecular dynamics: Principles and applications in combustion and energy systems,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.876571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4a61f264-4ef0-4662-a2a0-6c9e42d09d9b · outbound

This paper cites On the determination of molecular fields.—i. from the variation of the viscosity of a gas with temperature,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science On the determination of molecular fields.—i. from the variation of the viscosity of a gas with temperature,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.863850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ad169a17-776a-41b6-a3d2-ffca378b37fc · outbound

This paper cites Cohesion,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Cohesion,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.851435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation eaa5494c-eca2-4f95-8c63-e7a1c8cd4ec7 · outbound

This paper cites Embedded-atom method: Derivation and application to impu- rities, surfaces, and other defects in metals,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Embedded-atom method: Derivation and application to impu- rities, surfaces, and other defects in metals,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.838991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 76989deb-05c0-4760-8c10-79459b2e7dff · outbound

This paper cites Modified embedded-atom potentials for cubic materials and impurities,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Modified embedded-atom potentials for cubic materials and impurities,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.825013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b9efebb5-aac0-4f48-a32e-051897d2eab7 · outbound

This paper cites Reaxff: A reactive force field for hydrocarbons,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Reaxff: A reactive force field for hydrocarbons,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.811389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation aa4504d4-78ac-448d-8507-282763307a58 · outbound

This paper cites Charge optimized many-body potential for the for the si/sio2 system,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Charge optimized many-body potential for the for the si/sio2 system,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.797715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6ea13e55-3db4-44d9-8516-966ccf367593 · outbound

This paper cites Classical atomistic simulations of surfaces and heterogeneous interfaces with the charge-optimized many body (comb) potentials,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Classical atomistic simulations of surfaces and heterogeneous interfaces with the charge-optimized many body (comb) potentials,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.783604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 86f3b1d2-47d6-459e-89a1-994ef2203321 · outbound

This paper cites The radial distribution function for two-dimensional lennard- jones fluids: Computer simulation results,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science The radial distribution function for two-dimensional lennard- jones fluids: Computer simulation results,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.769940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4d416594-a5f1-42ef-b7e6-851902da03e0 · outbound

This paper cites Phase diagram of a lennard-jones system by molecular dynamics simulations,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Phase diagram of a lennard-jones system by molecular dynamics simulations,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.756152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3aef320d-53e6-4dd0-978a-1bdf3fe98067 · outbound

This paper cites The embedded-atom method: a review of theory and applications,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science The embedded-atom method: a review of theory and applications,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.742286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4ff9b291-d6a4-4eb8-8907-584ffe72f288 · outbound

This paper cites Variable charge many-body interatomic potentials,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Variable charge many-body interatomic potentials,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.728133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 36a652af-d0f0-4be9-8085-c6c16d8f3b25 · outbound

This paper cites Reactive potentials for advanced atomistic simulations,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Reactive potentials for advanced atomistic simulations,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.713494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 46526e9d-4f0c-45ad-afec-635b972d6871 · outbound

This paper cites Atomistic-scale analysis of carbon coating and its effect on the oxidation of aluminum nanoparticles by reaxff-molecular dynamics simulations,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Atomistic-scale analysis of carbon coating and its effect on the oxidation of aluminum nanoparticles by reaxff-molecular dynamics simulations,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.698726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 851de50b-8895-4e26-b202-790477075284 · outbound

This paper cites Charge op- timized many-body (COMB) potential for al2o3materials, interfaces, and nanostructures,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Charge op- timized many-body (COMB) potential for al2o3materials, interfaces, and nanostructures,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.685239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 995a98a7-713f-4665-aa61-1b86b91bd0cb · outbound

This paper cites Molecular dynamics study of hugoniot relation in shocked nickel single crystal,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Molecular dynamics study of hugoniot relation in shocked nickel single crystal,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.670943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 52007d26-2e9c-4615-a706-b46396d969ff · outbound

This paper cites Nanoindentation in alumina coated al: Molecular dynamics simulations and experiments,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Nanoindentation in alumina coated al: Molecular dynamics simulations and experiments,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.657603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e9edc596-e5f5-4a34-92b9-0b162cca6454 · outbound

This paper cites Crystal structure of a high-pressure/high-temperature phase of alumina by in situ x-ray diffraction,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science Crystal structure of a high-pressure/high-temperature phase of alumina by in situ x-ray diffraction,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.643590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 58f05b89-d350-4787-9211-374d844155fc · outbound

This paper cites First-principles calculation of kinetic barriers and metastabil- ity for the corundum-to-rh2o3 (ii) transition in al2o3,.

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science First-principles calculation of kinetic barriers and metastabil- ity for the corundum-to-rh2o3 (ii) transition in al2o3,

Reference 101

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:31:51.629165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers