2026-07-23T02:29:42Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:355 2020-04-02T00:00:00Z community-mcarchive openaire_data

Corminboeuf, Clémence Fabregat, Raimon Fabrizio, Alberto Meyer, Benjamin Hollas, Daniel Corminboeuf, Clémence 2020-04-02 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). application/gzip text/plain text/markdown https://doi.org/10.24435/materialscloud:2020.0033/v1 oai:materialscloud.org:355 mcid:2020.0033/v1 eng Materials Cloud https://doi.org/10.1021/acs.jctc.0c00100 https://doi.org/10.5281/zenodo.3630553 https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:vn-8p info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode EPFL MARVEL/DD1 Machine Learning Accelerated Sampling ERC Hamiltonian Replica Exchange Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry info:eu-repo/semantics/other