Search Results for author: Mordechai Kornbluth

Found 4 papers, 3 papers with code

Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics

2 code implementations11 Apr 2022 Albert Musaelian, Simon Batzner, Anders Johansson, Lixin Sun, Cameron J. Owen, Mordechai Kornbluth, Boris Kozinsky

This work introduces Allegro, a strictly local equivariant deep learning interatomic potential that simultaneously exhibits excellent accuracy and scalability of parallel computation.

Atomic Forces

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

1 code implementation8 Jan 2021 Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, Boris Kozinsky

This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations.

Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems

no code implementations7 May 2019 Jonathan P. Mailoa, Mordechai Kornbluth, Simon L. Batzner, Georgy Samsonidze, Stephen T. Lam, Chris Ablitt, Nicola Molinari, Boris Kozinsky

Neural network force field (NNFF) is a method for performing regression on atomic structure-force relationships, bypassing expensive quantum mechanics calculation which prevents the execution of long ab-initio quality molecular dynamics simulations.

Atomic Forces

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