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Crawford B, Timalsina U, Quach CD, Craven NC, Gilmer JB, McCabe C, Cummings PT, Potoff JJ. MoSDeF-GOMC: Python Software for the Creation of Scientific Workflows for the Monte Carlo Simulation Engine GOMC. J Chem Inf Model 2023; 63:1218-1228. [PMID: 36791286 DOI: 10.1021/acs.jcim.2c01498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
MoSDeF-GOMC is a python interface for the Monte Carlo software GOMC to the Molecular Simulation Design Framework (MoSDeF) ecosystem. MoSDeF-GOMC automates the process of generating initial coordinates, assigning force field parameters, and writing coordinate (PDB), connectivity (PSF), force field parameter, and simulation control files. The software lowers entry barriers for novice users while allowing advanced users to create complex workflows that encapsulate simulation setup, execution, and data analysis in a single script. All relevant simulation parameters are encoded within the workflow, ensuring reproducible simulations. MoSDeF-GOMC's capabilities are illustrated through a number of examples, including prediction of the adsorption isotherm for CO2 in IRMOF-1, free energies of hydration for neon and radon over a broad temperature range, and the vapor-liquid coexistence curve of a four-component surrogate for the jet fuel S-8. The MoSDeF-GOMC software is available on GitHub at https://github.com/GOMC-WSU/MoSDeF-GOMC.
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Affiliation(s)
- Brad Crawford
- Department of Chemical Engineering, Wayne State University, Detroit, Michigan 48202-4050, United States
| | - Umesh Timalsina
- Institute for Software Integrated Systems (ISIS), Vanderbilt University, Nashville, Tennessee 37212, United States
| | - Co D Quach
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, Tennessee 37235-1604, United States.,Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, Tennessee 37212, United States
| | - Nicholas C Craven
- Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, Tennessee 37212, United States.,Interdisciplinary Material Science Program, Vanderbilt University, Nashville, Tennessee 37235-0106, United States
| | - Justin B Gilmer
- Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, Tennessee 37212, United States.,Interdisciplinary Material Science Program, Vanderbilt University, Nashville, Tennessee 37235-0106, United States
| | - Clare McCabe
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, Tennessee 37235-1604, United States.,Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, Tennessee 37212, United States
| | - Peter T Cummings
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, Tennessee 37235-1604, United States.,Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, Tennessee 37212, United States
| | - Jeffrey J Potoff
- Department of Chemical Engineering, Wayne State University, Detroit, Michigan 48202-4050, United States
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Befort BJ, DeFever RS, Tow GM, Dowling AW, Maginn EJ. Machine Learning Directed Optimization of Classical Molecular Modeling Force Fields. J Chem Inf Model 2021; 61:4400-4414. [PMID: 34402301 DOI: 10.1021/acs.jcim.1c00448] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Accurate force fields are necessary for predictive molecular simulations. However, developing force fields that accurately reproduce experimental properties is challenging. Here, we present a machine learning directed, multiobjective optimization workflow for force field parametrization that evaluates millions of prospective force field parameter sets while requiring only a small fraction of them to be tested with molecular simulations. We demonstrate the generality of the approach and identify multiple low-error parameter sets for two distinct test cases: simulations of hydrofluorocarbon (HFC) vapor-liquid equilibrium (VLE) and an ammonium perchlorate (AP) crystal phase. We discuss the challenges and implications of our force field optimization workflow.
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Affiliation(s)
- Bridgette J Befort
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Ryan S DeFever
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Garrett M Tow
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Alexander W Dowling
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Edward J Maginn
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
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