876 citations · 1k across the 6 of their papers we have counts for
3 papers · 1 filter
Improving Molecular Force Fields Across Configurational Space by Combining Supervised and Unsupervised Machine Learning
Gregory Fonseca, Igor Poltavsky, Valentin Vassilev-Galindo +1
The training set of atomic configurations is key to the performance of any Machine Learning Force Field (MLFF) and, as such, the training set selection determines the applicability…
Challenges for Machine Learning Force Fields in Reproducing Potential Energy Surfaces of Flexible Molecules
Valentin Vassilev-Galindo, Gregory Fonseca, Igor Poltavsky +1
Dynamics of flexible molecules are often determined by an interplay between local chemical bond fluctuations and conformational changes driven by long-range electrostatics and van…
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
John A. Keith, Valentin Vassilev-Galindo, Bingqing Cheng +4
Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from…