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

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ID: 101771
2020
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Abstract
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 shows 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).
Reference Key
fabregat2020hamiltonianreservoirjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Fabregat, Raimon;Fabrizio, Alberto;Meyer, Benjamin;Hollas, Daniel;Corminboeuf, Clémence;
Journal journal of chemical theory and computation
Year 2020
DOI
10.1021/acs.jctc.0c00100
URL
Keywords
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