Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry

This work combines a machine learning potential energy function with a modular enhanced sampling scheme to obtain statistically converged thermodynamical properties of flexible medium size organic molecules at high ab initio level. We offer a modular environment in the python package MORESIM that allows custom design of replica exchange simulations with any level of theory including ML-based potentials. Our specific combination of Hamiltonian and reservoir replica exchange is shown to be a powerful technique to accelerate enhanced sampling simulations and explore free energy landscapes with a quantum chemical accuracy unattainable otherwise (e.g., DLPNO-CCSD(T)/CBS quality). This engine is used to demonstrate the relevance of accessing the ab initio free energy landscapes of molecules whose stability is determined by a subtle interplay between variations in the underlying potential energy and conformational entropy (i.e., a bridged asymmetrically polarized dithiacyclophane and a widely used organocatalyst) both in the gas phase and in solution (implicit solvent).

Identifier
Source https://archive.materialscloud.org/record/2020.0033/v1
Metadata Access https://archive.materialscloud.org/xml?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:355
Provenance
Creator Fabregat, Raimon; Fabrizio, Alberto; Meyer, Benjamin; Hollas, Daniel; Corminboeuf, Clémence
Publisher Materials Cloud
Publication Year 2020
Rights info:eu-repo/semantics/openAccess; Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode
OpenAccess true
Contact archive(at)materialscloud.org
Representation
Language English
Resource Type Dataset
Discipline Materials Science and Engineering