activity
20132026
most citedANI-1: An extensible neural network potential with DFT accuracy at force field computational cost

2.1k citations · 3.9k across the 15 of their papers we have counts for

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Showing 2020Show all

2 papers · 1 filter

physics.comp-ph2020★ 7 cited

Simple and efficient algorithms for training machine learning potentials to force data

Justin S. Smith, Nicholas Lubbers, Aidan P. Thompson +1

Abstract Machine learning models, trained on data from ab initio quantum simulations, are yielding molecular dynamics potentials with unprecedented accuracy. One limiting factor is…

cond-mat.mtrl-sci2020★ 98 cited

Automated discovery of a robust interatomic potential for aluminum

Justin S. Smith, Benjamin Nebgen, Nithin Mathew +8

Accuracy of molecular dynamics simulations depends crucially on the interatomic potential used to generate forces. The gold standard would be first-principles quantum mechanics (QM…