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Mordechai Kornbluth

Yeshiva University

22

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Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics.

2022 CoRR
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SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials. (arXiv:2101.03164v1 [physics.comp-ph])

2021
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SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials

2021 arXiv: Computational Physics
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Tensor-Field Molecular Dynamics - A Highly Accurate and Data-Efficient Interatomic Potential from SE(3)-equivariant Graph Neural Networks

2021 Bulletin of the American Physical Society
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Accurate and scalable multi-element graph neural network force field and molecular dynamics with direct force architecture

2020 arXiv: Computational Physics
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Spectral denoising for unsupervised analysis of correlated ionic transport

2020 arXiv: Computational Physics
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A community-powered search of machine learning strategy space to find NMR property prediction models.

2020 arXiv: Computational Physics
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A group theoretical approach to computing phonons and their interactions

2019 arXiv: Computational Physics
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An optimal approach to computing phonons and their interactions via finite difference

2018 Bulletin of the American Physical Society
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Efficient Computation of Anharmonic Force Constants via q-space, with Application to Graphene

2017 Bulletin of the American Physical Society
- Mordechai Kornbluth
- Chris A. Marianetti
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