341 citations · 348 across the 2 of their papers we have counts for
2 papers
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…
physics.chem-ph2017★ 341 cited
ANI-1: A data set of 20M off-equilibrium DFT calculations for organic molecules
Justin S. Smith, Olexandr Isayev, Adrian E. Roitberg
One of the grand challenges in modern theoretical chemistry is designing and implementing approximations that expedite ab initio methods without loss of accuracy. Machine learning…