materialscloud:2020.0033/v1

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

Raimon Fabregat1, Alberto Fabrizio1, Benjamin Meyer1, Daniel Hollas1, Clémence Corminboeuf1*

1 Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland

* Corresponding authors emails: clemence.corminbouef@epfl.ch
DOI10.24435/materialscloud:2020.0033/v1 [version v1]

Publication date: Apr 02, 2020, 00:00:00

How to cite this record

Raimon Fabregat, Alberto Fabrizio, Benjamin Meyer, Daniel Hollas, Clémence Corminboeuf, Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry, Materials Cloud Archive 2020.0033/v1 (2020), doi: 10.24435/materialscloud:2020.0033/v1.

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).

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resHRE_paper_data.tar.gz
MD5md5:53a73a81a57b8d1af2cb7507db7a079c
254.2 MiB Tar ball containing structures and energies (for a more detailed description of the content see README.txt)
README.txt
MD5md5:3cb075e0b761009d27c6ac13e38e3cd9
2.4 KiB Details of the content of the resHRE_paper_data.tar.gz file

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.

Keywords

EPFL MARVEL/DD1 Machine Learning Accelerated Sampling ERC Hamiltonian Replica Exchange

Version history:

2020.0033/v1 (version v1) [This version] Apr 02, 2020, 00:00:00 DOI10.24435/materialscloud:2020.0033/v1