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


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{
  "id": "355", 
  "updated": "2020-09-10T13:59:08.408446+00:00", 
  "metadata": {
    "version": 1, 
    "contributors": [
      {
        "givennames": "Raimon", 
        "affiliations": [
          "Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Fabregat"
      }, 
      {
        "givennames": "Alberto", 
        "affiliations": [
          "Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Fabrizio"
      }, 
      {
        "givennames": "Benjamin", 
        "affiliations": [
          "Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Meyer"
      }, 
      {
        "givennames": "Daniel", 
        "affiliations": [
          "Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Hollas"
      }, 
      {
        "givennames": "Cl\u00e9mence", 
        "affiliations": [
          "Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
        ], 
        "email": "clemence.corminbouef@epfl.ch", 
        "familyname": "Corminboeuf"
      }
    ], 
    "title": "Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry", 
    "_oai": {
      "id": "oai:materialscloud.org:355"
    }, 
    "keywords": [
      "EPFL", 
      "MARVEL/DD1", 
      "Machine Learning", 
      "Accelerated Sampling", 
      "ERC", 
      "Hamiltonian Replica Exchange"
    ], 
    "publication_date": "Apr 02, 2020, 00:00:00", 
    "_files": [
      {
        "key": "resHRE_paper_data.tar.gz", 
        "description": "Tar ball containing structures and energies (for a more detailed description of the content see README.txt)", 
        "checksum": "md5:53a73a81a57b8d1af2cb7507db7a079c", 
        "size": 266499589
      }, 
      {
        "key": "README.txt", 
        "description": "Details of the content of the resHRE_paper_data.tar.gz file", 
        "checksum": "md5:3cb075e0b761009d27c6ac13e38e3cd9", 
        "size": 2444
      }
    ], 
    "references": [
      {
        "doi": "10.1021/acs.jctc.0c00100", 
        "citation": "R. Fabregat, A. Fabrizio, B. Meyer, D. Hollas, C. Corminboeuf, J. Chem. Theory Comput., 16, 3084-3094 (2020)", 
        "url": "https://pubs.acs.org/doi/10.1021/acs.jctc.0c00100", 
        "type": "Journal reference"
      }, 
      {
        "comment": "MORESIM software as described in the paper.", 
        "doi": "10.5281/zenodo.3630553", 
        "citation": "R. Fabregat, A. Fabrizio, B. Meyer, D. Hollas, C. Corminboeuf, MORESIM (Version v1.1). Zenodo", 
        "url": "https://zenodo.org/record/3630553", 
        "type": "Software"
      }
    ], 
    "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).", 
    "status": "published", 
    "license": "Creative Commons Attribution 4.0 International", 
    "conceptrecid": "354", 
    "is_last": true, 
    "mcid": "2020.0033/v1", 
    "edited_by": 22, 
    "id": "355", 
    "owner": 22, 
    "license_addendum": "", 
    "doi": "10.24435/materialscloud:2020.0033/v1"
  }, 
  "revision": 2, 
  "created": "2020-05-12T13:53:52.302024+00:00"
}