Modeling molecular ensembles with gradient-domain machine learning force fields
Alex M. Maldonado, Igor Poltavsky, Valentín Vassilev-Galindo, Alexandre Tkatchenko, John A. Keith
Abstract
Gradient-domain machine learning (GDML) force fields show excellent accuracy, data efficiency, and applicability for molecules, and a many-body approach opens the possibility of increased transferability to molecular ensembles.
Topics & Concepts
TransferabilityForce field (fiction)Domain (mathematical analysis)Computer scienceArtificial intelligenceField (mathematics)Machine learningMathematicsMathematical analysisLogitPure mathematicsMachine Learning in Materials ScienceComputational Drug Discovery MethodsProtein Structure and Dynamics