1.9k citations · 2k across the 11 of their papers we have counts for
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A community-powered search of machine learning strategy space to find NMR property prediction models
Lars A. Bratholm, Will Gerrard, Brandon Anderson +26
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML st…
Accurate and scalable multi-element graph neural network force field and molecular dynamics with direct force architecture
Cheol Woo Park, Mordechai Kornbluth, Jonathan Vandermause +3
Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural n…
Spectral denoising for unsupervised analysis of correlated ionic transport
Nicola Molinari, Yu Xie, Ian Leifer +3
Computation of correlated ionic transport properties from molecular dynamics in the Green-Kubo formalism is expensive as one cannot rely on the affordable mean square displacement…