<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Fabregat, Raimon</dc:creator> <dc:creator>Fabrizio, Alberto</dc:creator> <dc:creator>Meyer, Benjamin</dc:creator> <dc:creator>Hollas, Daniel</dc:creator> <dc:creator>Corminboeuf, Clémence</dc:creator> <dc:date>2020-04-02</dc:date> <dc:description>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).</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2020.0033/v1</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:2020.0033/v1</dc:identifier> <dc:identifier>mcid:2020.0033/v1</dc:identifier> <dc:identifier>oai:materialscloud.org:355</dc:identifier> <dc:language>en</dc:language> <dc:publisher>Materials Cloud</dc:publisher> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:subject>EPFL</dc:subject> <dc:subject>MARVEL/DD1</dc:subject> <dc:subject>Machine Learning</dc:subject> <dc:subject>Accelerated Sampling</dc:subject> <dc:subject>ERC</dc:subject> <dc:subject>Hamiltonian Replica Exchange</dc:subject> <dc:title>Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>