chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations | Paper Citation Record

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

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This paper cites The Journal of chemical physics145(17) (2016).

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations The Journal of chemical physics145(17) (2016)

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations org/10.1073/pnas.0500193102

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

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This paper cites Chemical Science 8(4), 3192–3203 (2017) https://doi.org/10.1039/C6SC05720A.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Chemical Science 8(4), 3192–3203 (2017) https://doi.org/10.1039/C6SC05720A

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This paper cites In: Precup, D., Teh, Y.W.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations In: Precup, D., Teh, Y.W

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This paper cites Physical Review B99(1), 014104 (2019) https://doi.org/10.1103/ PhysRevB.99.014104.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Physical Review B99(1), 014104 (2019) https://doi.org/10.1103/ PhysRevB.99.014104

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Orb-v3: atomistic simulation at scale

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations https://doi.org/10.48550/arXiv

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

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This paper cites Nature Machine Intelligence7(1), 56–67 (2025) 19 https://doi.org/10.1038/s42256-024-00956-x.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Nature Machine Intelligence7(1), 56–67 (2025) 19 https://doi.org/10.1038/s42256-024-00956-x

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This paper cites Nature Communications14(1), 579 (2023) https://doi.org/10.1038/ s41467-023-36329-y.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Nature Communications14(1), 579 (2023) https://doi.org/10.1038/ s41467-023-36329-y

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This paper cites Nature Communications 13(1), 1–11 (2022) https://doi.org/10.1038/s41467-022-29939-5.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Nature Communications 13(1), 1–11 (2022) https://doi.org/10.1038/s41467-022-29939-5

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This paper cites SchNet - a deep learning architecture for molecules and materials.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations SchNet - a deep learning architecture for molecules and materials

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Equivariant message passing for the prediction of tensorial properties and molecular spectra

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations jcim.0c00451

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations https://doi.org/10.48550/ arXiv.2506.02023

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Computer Physics Communications271, 108171 (2022) https: //doi.org/10.1016/j.cpc.2021.108171

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Automated discovery of a robust interatomic potential for aluminum

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations https://doi

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations 1038/s41467-021-27241-4

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Ewald-based Long-Range Message Passing for Molecular Graphs

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Extending the RANGE of Graph Neural Networks: Relaying Attention Nodes for Global Encoding

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations https://doi

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

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Thermal Conductivity Predictions with Foundation Atomistic Models

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Universal Machine Learning Interatomic Potentials are Ready for Phonons

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Computer Physics Communications310, 109512 (2025) https://doi.org/10.1016/j.cpc.2025.109512

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations https://github.com/openxla/xla

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations https://openxla.org/xla/pjrt

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations https://doi.org/10.5281/zenodo.15009305

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Modelling and Simulation in Materials Science and Engineering27(8), 085015 (2019) https://doi.org/10.1088/ 1361-651X/ab4b36

Reference 59

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING18(015012) (2010) https://doi

Reference 60

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Reference 61

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Reference 62

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules

Reference 63

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

Reference 64

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations https://doi.org/10.26434/chemrxiv-2024-bdfr0

Reference 65

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

Reference 66

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Observation f433996f-8138-45b4-830e-d00c86c9fc26 · outbound

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations The Journal of chemical physics153(4) (2020)

Reference 67

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Observation c61879ad-3e22-48bd-8894-945dc7d7d251 · outbound

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations The Journal of Chemical Physics97(3), 1990–2001 (1992) https: //doi.org/10.1063/1.463137 24

Reference 68

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Physical Review Letters98(14), 146401 (2007) https://doi.org/10.1103/PhysRevLett.98.146401

Reference 69

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Chem- ical Reviews121(16), 9759–9815 (2021) https://doi.org/10.1021/acs.chemrev

Reference 70

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Physical Review B87(18), 184115 (2013) https://doi.org/10.1103/PhysRevB.87

Reference 71

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Directional Message Passing for Molecular Graphs

Reference 72

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

Reference 73

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Observation 2da25387-6a79-4e05-94da-451c83cc2405 · outbound

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

Reference 74

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Pith citing papers