AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials | Paper Citation Record

Observation 78878558-0775-436d-b270-5c59200fd64b · outbound

This paper cites Generalized Neural-Network Representation of High-Dimensional Potential- Energy Surfaces.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Generalized Neural-Network Representation of High-Dimensional Potential- Energy Surfaces

Reference 1

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Observation d2a0b9f3-20aa-4ab0-98fa-14b6c01fc137 · outbound

This paper cites Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

Reference 2

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Observation dcb20627-cb26-47af-bbe0-1ba790c9d57d · outbound

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

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Spectral neighbor analysis method for automated generation of quantum-accurate inter- atomic potentials

Reference 3

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Observation a3a171f2-4ae9-4bce-bed9-26cbbeb26c1e · outbound

This paper cites Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Poten- tials.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Poten- tials

Reference 4

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Observation e94bb6d0-cf68-48f8-8c7e-e9464549f12b · outbound

This paper cites Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

Reference 5

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Observation c3c48f04-4a82-44b6-b29b-934ce8eeb031 · outbound

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

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Atomic cluster expansion for accurate and transferable interatomic potentials

Reference 6

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Observation 06096050-0777-4662-9737-1f4ae07e8681 · outbound

This paper cites Physically informed artificial neural networks for atomistic modeling of materials.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Physically informed artificial neural networks for atomistic modeling of materials

Reference 7

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Observation 0d660b52-558e-49c2-a975-41bfc77b43fb · outbound

This paper cites Machine Learning Force Fields.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Machine Learning Force Fields

Reference 8

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Observation 1df5ed89-3cf0-4244-81b6-a1cb77945b0b · outbound

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

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials E(3)-equivariant graph neural networks for data-efficient and accurate interatomic poten- tials

Reference 9

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Observation 44bd15d3-5fbf-464b-ab07-c979cedb41fb · outbound

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

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

Reference 10

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Observation 719eccf4-8e40-4182-8566-233d7590681c · outbound

This paper cites GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations

Reference 11

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Observation 5d162c3e-4888-4caa-a01e-328a8ed1fe9d · outbound

This paper cites Equivariant tensor network potentials.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Equivariant tensor network potentials

Reference 12

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Observation 31408977-89c0-4c18-95fd-b49f35054905 · outbound

This paper cites E(n)-Equivariant cartesian tensor message passing interatomic potential.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials E(n)-Equivariant cartesian tensor message passing interatomic potential

Reference 13

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Observation 5287f16d-c9d7-4ee1-a1cf-e8569c96788d · outbound

This paper cites Recent Advances in Machine Learning-Assisted Multiscale Design of Energy Materials.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Recent Advances in Machine Learning-Assisted Multiscale Design of Energy Materials

Reference 14

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Observation 52ce7243-bc5b-4a7d-b45a-95442e0d1097 · outbound

This paper cites Modelingrefractoryhigh-entropyalloyswithefficientmachine- learned interatomic potentials: Defects and segregation.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Modelingrefractoryhigh-entropyalloyswithefficientmachine- learned interatomic potentials: Defects and segregation

Reference 15

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Observation 8fb7172e-4dc2-48ad-8293-6296f82e1125 · outbound

This paper cites Ab initio framework for deciphering trade-off relationships in multi-component alloys.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Ab initio framework for deciphering trade-off relationships in multi-component alloys

Reference 16

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Observation 53b6e0a2-b79c-4b9b-acb7-4d171d01016b · outbound

This paper cites General-purpose machine-learned potential for 16 elemental metals and their alloys.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials General-purpose machine-learned potential for 16 elemental metals and their alloys

Reference 17

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Observation c4b480f6-fc1a-44d6-b1e1-830395b3ecbe · outbound

This paper cites Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential

Reference 18

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Observation b274b169-b72e-4700-86f2-102db37afb55 · outbound

This paper cites Machine learning interatomic potential with DFT accuracy for general grain boundaries in α-Fe.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Machine learning interatomic potential with DFT accuracy for general grain boundaries in α-Fe

Reference 19

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Observation c7b4676a-1064-44e2-9faf-d4f964b3c874 · outbound

This paper cites Machine-learning potentials for nanoscale simulations of tensile deformation and fracture in ceramics.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Machine-learning potentials for nanoscale simulations of tensile deformation and fracture in ceramics

Reference 20

Resolution: verified exact; doi, observed 2026-08-03T13:03:26.927567Z

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correction dated 2024-04-18. Source: crossref record 10.1038/s41524-024-01276-9->10.1038/s41524-024-01252-3:correction, observed 2026-07-11T03:06:21.039674+00:00. This notice travels one citation hop only.

Observation ad6a0bba-f4f2-4a83-a4c2-7e787fe28e88 · outbound

This paper cites Learning from models: high-dimensional analyses on the performance of machine learning interatomic potentials.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Learning from models: high-dimensional analyses on the performance of machine learning interatomic potentials

Reference 21

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Observation adbc4a81-5d0f-4e54-ae3b-afbc74b3dd32 · outbound

This paper cites Erhard et al.How Realistic are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper- Water Interfaces.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Erhard et al.How Realistic are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper- Water Interfaces

Reference 22

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Observation f928b256-a955-4a27-b436-79f50176f2b8 · outbound

This paper cites Modeling extensive defects in metals through classical potential-guided sampling and auto- mated configuration reconstruction.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Modeling extensive defects in metals through classical potential-guided sampling and auto- mated configuration reconstruction

Reference 23

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Observation 4babea76-5f8d-4258-a71b-e31a8bdc4345 · outbound

This paper cites Active Learning Literature Survey.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Active Learning Literature Survey

Reference 24

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Observation 25661e26-75e3-4ac5-a445-bf750bccae75 · outbound

This paper cites Active learning of linearly parametrized interatomic po- tentials.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Active learning of linearly parametrized interatomic po- tentials

Reference 25

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Observation dad63388-3beb-4ce0-b62c-ad2928f40794 · outbound

This paper cites Active learning of uniformly accurate interatomic potentials for materials simulation.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Active learning of uniformly accurate interatomic potentials for materials simulation

Reference 26

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Observation 708252f2-4785-4716-9065-abaa104c085b · outbound

This paper cites On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

Reference 27

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Observation 64cce5ea-f894-4fef-8a94-04291ba6e7ac · outbound

This paper cites Inoperandoactivelearningofinteratomicinteractionduringlarge-scalesimulations.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Inoperandoactivelearningofinteratomicinteractionduringlarge-scalesimulations

Reference 28

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Observation 5271c5d7-f5ed-4722-9265-8032e2c423d4 · outbound

This paper cites Nanohardness from First Principles with Active Learning on Atomic Environ- ments.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Nanohardness from First Principles with Active Learning on Atomic Environ- ments

Reference 29

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Observation e49e7edf-ba72-4760-a39d-88cf69baf13c · outbound

This paper cites Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning

Reference 30

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Observation e2a9d867-25de-47da-87dc-7f6b6d706b16 · outbound

This paper cites Actively trained magnetic moment tensor potentials for mechanical, dynamical, and thermal properties of paramagnetic CrN.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Actively trained magnetic moment tensor potentials for mechanical, dynamical, and thermal properties of paramagnetic CrN

Reference 31

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Observation 670150c6-3121-4c09-9e03-fc5f2643b587 · outbound

This paper cites https://www.materialscloud.org/home.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials https://www.materialscloud.org/home

Reference 39

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AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials AiiDA: automated interactive infrastructure and database for computational science

Reference 40

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This paper cites The Potential of Atomistic Simulations and the Knowledgebase of Interatomic Models.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials The Potential of Atomistic Simulations and the Knowledgebase of Interatomic Models

Reference 42

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Observation b61c019f-7373-4132-bca1-3d5bf99b6399 · outbound

This paper cites ColabFit exchange: Open-access datasets for data-driven interatomic potentials.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials ColabFit exchange: Open-access datasets for data-driven interatomic potentials

Reference 43

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Observation 8e45b865-fe79-403a-bad1-13c57ea718d6 · outbound

This paper cites Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows

Reference 44

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AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials pyiron: An integrated development environment for computational materials science

Reference 45

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Observation d37597c3-05d5-42ff-8a6a-3e33ff03f6b9 · outbound

This paper cites Machine learning of molecular prop- erties: Locality and active learning.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Machine learning of molecular prop- erties: Locality and active learning

Reference 48

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Observation 1f04748d-c41d-48f8-93af-dbf42faf4bc8 · outbound

This paper cites How to Find a Good Submatrix.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials How to Find a Good Submatrix

Reference 49

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This paper cites The MLIP package: moment tensor potentials with MPI and active learning.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials The MLIP package: moment tensor potentials with MPI and active learning

Reference 50

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This paper cites Machine-learning potentials enable predictive and tractable high-throughput screening of random alloys.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Machine-learning potentials enable predictive and tractable high-throughput screening of random alloys

Reference 51

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This paper cites AI-accelerated materials informatics method for the discovery of ductile alloys.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials AI-accelerated materials informatics method for the discovery of ductile alloys

Reference 52

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AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Apache License 2.0, open-source project

Reference 53

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AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Apache License 2.0

Reference 54

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AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Apache License 2.0

Reference 55

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AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Apache License 2.0

Reference 56

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Observation 8ec9c785-da62-4585-a2cc-654b70dcde5a · outbound

This paper cites Apache License 2.0.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Apache License 2.0

Reference 57

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This paper cites Apache License 2.0.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Apache License 2.0

Reference 58

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AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials BSD 3-Clause License

Reference 59

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AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials https://gitlab.com/ganciaux/blackdynamite

Reference 60

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AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Version 5.x

Reference 61

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AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Networked Storage Server for ZODB

Reference 62

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Observation 3177fb3b-396f-4669-b057-91a4c5ec4fab · outbound

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

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials The atomic simulation environment—a Python library for working with atoms

Reference 63

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Observation 149eae0e-cd87-4f49-be2b-ed5c5cb7c7b4 · outbound

This paper cites Efficient iterative schemes forab initiototal-energy calculations using a plane- wave basis set.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Efficient iterative schemes forab initiototal-energy calculations using a plane- wave basis set

Reference 64

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AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Projector augmented-wave method

Reference 65

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Observation 11bb00d0-5fbc-4516-9c7c-269d67be2c5e · outbound

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

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials From ultrasoft pseudopotentials to the projector augmented-wave method

Reference 66

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AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Generalized Gradient Approximation Made Simple

Reference 67

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This paper cites Automated atomistic simulations of dissociated dislocations withab initioaccu- racy.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Automated atomistic simulations of dissociated dislocations withab initioaccu- racy

Reference 68

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Observation 5599d7a3-3e32-48f3-9956-d6cbc380c22b · outbound

This paper cites Machine-learning interatomic potential for radiation damage and defects in tungsten.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Machine-learning interatomic potential for radiation damage and defects in tungsten

Reference 69

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Observation df6f9a6e-d69b-414b-babb-742ea80ffab2 · outbound

This paper cites Frank-Read source operation in six body-centered cubic refractory metals.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Frank-Read source operation in six body-centered cubic refractory metals

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-03T13:00:45.611646Z

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Observation 8f400f3d-a0f0-44a5-b034-25b40d48df60 · outbound

This paper cites Theory of the core structures of dislocations in body-centered-cubic metals.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Theory of the core structures of dislocations in body-centered-cubic metals

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-03T13:00:45.669830Z

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Observation 85db2a0e-03cc-4899-8993-eedc206da776 · outbound

This paper cites Complex strengthening mechanisms in the NbMoTaW multi-principal element alloy.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Complex strengthening mechanisms in the NbMoTaW multi-principal element alloy

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-03T13:00:45.815656Z

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Observation 2bbd0b40-581c-4e89-a5c0-932b5a5994aa · outbound

This paper cites Exact average many-body interatomic interaction model for random alloys.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Exact average many-body interatomic interaction model for random alloys

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-03T13:00:45.949013Z

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Observation b647fee1-7bc5-410e-81c4-7ea5894f4b93 · outbound

This paper cites Ab-initio grain boundary thermodynamics beyond the dilute limit.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Ab-initio grain boundary thermodynamics beyond the dilute limit

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-03T13:00:46.072409Z

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Observation f82a5d35-a228-40ca-adc7-544401f28be4 · outbound

This paper cites Screening of generalized stacking fault energies, surface energies and intrinsic ductile potency of refractory multicomponent alloys.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Screening of generalized stacking fault energies, surface energies and intrinsic ductile potency of refractory multicomponent alloys

Reference 75

Resolution: malformed identifier; no resolver link, observed 2026-08-03T13:00:46.200542Z

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Observation 8d24832d-f95f-4692-b34e-922ea7e3bdc0 · outbound

This paper cites Bijjala, Susan R.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Bijjala, Susan R

Reference 76

Resolution: verified exact; arxiv_id, observed 2026-08-03T13:03:26.644339Z

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Observation baa0c175-1edf-40e4-b708-12137dca8af1 · outbound

This paper cites Uncertainty-driven dynamics for active learning of interatomic potentials.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Uncertainty-driven dynamics for active learning of interatomic potentials

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-03T13:00:46.483217Z

Source-reported events for the cited work

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Observation 11ee53f6-17ee-4ccd-baa4-efea0f4563f4 · outbound

This paper cites Calibration of uncertainty in the active learning of machine learning force fields.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Calibration of uncertainty in the active learning of machine learning force fields

Reference 78

Resolution: verified exact; doi, observed 2026-08-03T13:03:26.525143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cd5e40ef-c9b2-407b-82d2-d80637261bfc · outbound

This paper cites an unresolved cited work.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Unresolved cited work

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-03T13:00:46.829448Z

Source-reported events for the cited work

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Observation 60939b17-fb17-481c-9ca6-ac84491668ee · outbound

This paper cites Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-03T13:00:47.017183Z

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Observation ba14f111-e173-4a06-bc71-568cb7d190fc · outbound

This paper cites matscipy: materials science at the atomic scale withPython.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials matscipy: materials science at the atomic scale withPython

Reference 81

Resolution: verified exact; doi, observed 2026-08-03T13:03:26.483664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 66b2bcdf-c128-4e8c-9d0c-fcd649d8f16c · outbound

This paper cites Structural Relaxation Made Simple.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Structural Relaxation Made Simple

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-03T13:00:47.345983Z

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Observation 18cc39f8-ecd0-429a-86b2-0861d59d542f · outbound

This paper cites Improved tangent estimate in the nudged elastic band method for finding minimum energy paths and saddle points.

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Improved tangent estimate in the nudged elastic band method for finding minimum energy paths and saddle points

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-03T13:00:47.490974Z

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