Electrostatic embedding machine learning for ground and excited state molecular dynamics of solvated molecules
Patrizia Mazzeo, Edoardo Cignoni, Amanda Arcidiacono, Lorenzo Cupellini, Benedetta Mennucci
Abstract
We propose a strategy to perform electrostatic embedding machine learning (ML)/molecular mechanics (MM) molecular dynamics simulations.
Topics & Concepts
Excited stateGround stateEmbeddingMoleculeMolecular dynamicsChemical physicsDynamics (music)PhysicsComputer scienceChemistryComputational chemistryAtomic physicsArtificial intelligenceQuantum mechanicsAcousticsMachine Learning in Materials ScienceSpectroscopy and Quantum Chemical Studies