52 citations · 52 across the 3 of their papers we have counts for
3 papers
Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields Under the Microscope
Gregory Fonseca, Igor Poltavsky, Alexandre Tkatchenko
As the sophistication of Machine Learning Force Fields (MLFF) increases to match the complexity of extended molecules and materials, so does the need for tools to properly analyze…
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…