Simulating solvation and acidity in complex mixtures with first-principles accuracy: the case of CH₃SO₃H and H₂O₂ in phenol


JSON Export

{
  "id": "781", 
  "metadata": {
    "title": "Simulating solvation and acidity in complex mixtures with first-principles accuracy: the case of CH\u2083SO\u2083H and H\u2082O\u2082 in phenol", 
    "doi": "10.24435/materialscloud:2x-7x", 
    "license": "Creative Commons Attribution 4.0 International", 
    "keywords": [
      "machine learning", 
      "solution chemistry", 
      "acid homogeneous catalysis", 
      "catalysis", 
      "acid", 
      "artificial intelligence", 
      "reaction", 
      "CH3SO3H", 
      "H2O2", 
      "MARVEL"
    ], 
    "contributors": [
      {
        "affiliations": [
          "Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, 1015, Switzerland"
        ], 
        "familyname": "Rossi", 
        "email": "kevin.rossi@epfl.ch", 
        "givennames": "Kevin"
      }, 
      {
        "affiliations": [
          "Laboratory for Computational Molecular Design (LCMD), Institute of Chemical Sciences and Engineering, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, 1015, Switzerland"
        ], 
        "familyname": "Juraskova", 
        "email": "veronika.juraskova@epfl.ch", 
        "givennames": "Veronika"
      }, 
      {
        "affiliations": [
          "Eco-Efficient Products and Processes Laboratory, Solvay, RIC Shanghai, China"
        ], 
        "familyname": "Wischert", 
        "givennames": "Raphael"
      }, 
      {
        "affiliations": [
          "Aroma Performance Laboratory, Solvay, RIC Lyon, France"
        ], 
        "familyname": "Garel", 
        "givennames": "Laurent"
      }, 
      {
        "affiliations": [
          "Laboratory for Computational Molecular Design (LCMD), Institute of Chemical Sciences and Engineering, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, 1015, Switzerland"
        ], 
        "familyname": "Corminboeuf", 
        "email": "clemence.corminboeuf@epfl.ch", 
        "givennames": "Clemence"
      }, 
      {
        "affiliations": [
          "Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, 1015, Switzerland"
        ], 
        "familyname": "Ceriotti", 
        "email": "michele.ceriotti@epfl.ch", 
        "givennames": "Michele"
      }
    ], 
    "_files": [
      {
        "description": "i-pi input to run basic MD (https://github.com/cosmo-epfl/i-pi)", 
        "checksum": "md5:012f438cd8a0d09e1ae32cd583c7a09a", 
        "size": 3779, 
        "key": "input.xml"
      }, 
      {
        "description": "example PBE input for DFT calculations (https://www.cp2k.org/)", 
        "checksum": "md5:08e632a33d5afb551b9d5f704ceced55", 
        "size": 1587, 
        "key": "pbe.cp2k"
      }, 
      {
        "description": "exemple PBE0 input for DFT calculations (https://www.cp2k.org/)", 
        "checksum": "md5:9a47c035c868b0ccb78729729345949c", 
        "size": 3058, 
        "key": "pbe0.cp2k"
      }, 
      {
        "description": "example DFTB+ input for DFTB calculations (https://www.dftbplus.org/)", 
        "checksum": "md5:8e253196041e84221c7cde407c6c5656", 
        "size": 1222, 
        "key": "dftb_in.hsd"
      }, 
      {
        "description": "example input.nn input to train and use a neural network for force and energy predictions (https://github.com/CompPhysVienna/n2p2)", 
        "checksum": "md5:e7acab8f9218b083a66d28a0bcda31a1", 
        "size": 23151, 
        "key": "input.nn"
      }, 
      {
        "description": "example LAMMPS input for MD calculations via i-pi and using neural network potentials (https://lammps.sandia.gov/)", 
        "checksum": "md5:ba8eae7e733c2cd14244540b26535f8e", 
        "size": 3547, 
        "key": "lmp1.in"
      }, 
      {
        "description": "Dataset for learning DFT energies and forces (input.data format as used in N2P2)", 
        "checksum": "md5:36254b945cfaf68c226e49644ce5e96f", 
        "size": 35960401, 
        "key": "input.data.direct.gz"
      }, 
      {
        "description": "Dataset for learning DFTB-baselined energies and forces (input.data format as used in N2P2)", 
        "checksum": "md5:d6b5b6ed0368757315593302a3df17ff", 
        "size": 37003974, 
        "key": "input.data.delta.gz"
      }, 
      {
        "description": "nn weights for direct predictions of forces and energies", 
        "checksum": "md5:8475cf5afa4b6543e57b845c96882a14", 
        "size": 146607, 
        "key": "direct.tar.gz"
      }, 
      {
        "description": "nn weights for DFTB-baselined predictions of forces and energies", 
        "checksum": "md5:68cde22ad92072c0452c7941ce792bb0", 
        "size": 730654, 
        "key": "delta.tar.gz"
      }, 
      {
        "description": "README", 
        "checksum": "md5:9dc6a1ab9a65080329ee332892c0460f", 
        "size": 375, 
        "key": "README.txt"
      }
    ], 
    "references": [
      {
        "type": "Journal reference", 
        "doi": "https://doi.org/10.1021/acs.jctc.0c00362", 
        "citation": "K. Rossi, V. Jur\u00e1skov\u00e1, R. Wischert, L. Garel, C. Corminb\u0153uf, M. Ceriotti, J. Chem. Theory Comput., 16, 8, 5139\u20135149 (2020)", 
        "comment": "Paper reference", 
        "url": "https://pubs.acs.org/doi/abs/10.1021/acs.jctc.0c00362"
      }
    ], 
    "conceptrecid": "433", 
    "version": 2, 
    "edited_by": 100, 
    "id": "781", 
    "owner": 132, 
    "mcid": "2021.50", 
    "is_last": true, 
    "status": "published", 
    "description": "Set of inputs to perform the calculations reported in the paper.\nThe i-pi input enables to perform molecular dynamics / metadynamics / REMD / PIMD simulations, with adequate thermostats.\nThe DFTB and LAMMPS input respectively enable to calculate force and energies within the DFTB and Neural Network Forcefield frameworks.\nThe CP2K input files enable to calculate force and energies at PBE and PBE0 level. The latter is used as the reference to train the neural network correction on top of DFTB.\n\nBrief description of the work: We present a generally-applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in explicit solvent using H\u2082O\u2082 and CH\u2083SO\u2083H in phenol as an example. In order to address the challenges posed by the complexity of the problem, we resort to a set of data-driven methods and enhanced sampling algorithms. The synergistic application of these techniques makes the first-principle estimation of the chemical properties feasible without renouncing to the use of explicit solvation, involving extensive statistical sampling. Ensembles of neural network potentials are trained on a set of configurations carefully selected out of preliminary simulations performed at a low-cost density-functional tight-binding (DFTB) level. Energy and forces of these configurations are then recomputed at the hybrid density functional theory (DFT) level and used to train the neural networks. The stability of the NN model is enhanced by using DFTB energetics as a baseline, but the efficiency of the direct NN (i.e., baseline-free) is exploited via a multiple-time step integrator. The neural network potentials are combined with enhanced sampling techniques, such as replica exchange and metadynamics, and used to characterize the relevant protonated species and dominant non-covalent interactions in the mixture, also considering nuclear quantum effects.", 
    "license_addendum": null, 
    "_oai": {
      "id": "oai:materialscloud.org:781"
    }, 
    "publication_date": "Mar 26, 2021, 18:24:05"
  }, 
  "revision": 6, 
  "updated": "2021-03-26T17:24:05.739235+00:00", 
  "created": "2021-03-19T17:39:20.419226+00:00"
}