2.1k citations · 3.9k across the 15 of their papers we have counts for
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Quantum-based Molecular Dynamics Simulations Using Tensor Cores
Joshua Finkelstein, Justin S. Smith, Susan M. Mniszewski +4
Tensor cores, along with tensor processing units, represent a new form of hardware acceleration specifically designed for deep neural network calculations in artificial intelligenc…
Mixed Precision Fermi-Operator Expansion on Tensor Cores From a Machine Learning Perspective
Joshua Finkelstein, Justin Smith, Susan M. Mniszewski +4
We present a second-order recursive Fermi-operator expansion scheme using mixed precision floating point operations to perform electronic structure calculations using tensor core u…
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…
Less is more: sampling chemical space with active learning
Justin S. Smith, Ben Nebgen, Nicholas Lubbers +2
The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train suc…