Δ-Machine Learned Potential Energy Surfaces and Force Fields
Joel M. Bowman, Chen Qu, Riccardo Conte, Apurba Nandi, Paul L. Houston, Qi Yu
Abstract
-methylacetamide, acetyl acetone, and ethanol. For 15-atom tropolone, it appears that special approaches (e.g., molecular tailoring, local CCSD(T)) are needed to obtain the CCSD(T) energies. A new aspect of this approach is the extension of Δ-machine learning to force fields. The approach is based on many-body corrections to polarizable force field potentials. This is examined in detail using the TTM2.1 water potential. The corrections make use of our recent CCSD(T) datasets for 2-b, 3-b, and 4-b interactions for water. These datasets were used to develop a new fully ab initio potential for water, termed q-AQUA.
Topics & Concepts
Computer sciencePotential energyForce field (fiction)Potential fieldEnergy (signal processing)NanotechnologyPhysicsArtificial intelligenceMaterials scienceClassical mechanicsGeophysicsQuantum mechanicsMachine Learning in Materials ScienceAdvanced Chemical Physics StudiesNuclear Physics and Applications