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MPMC and MCMD: Free High‐Performance Simulation Software for Atomistic Systems
Author(s) -
Franz Douglas M.,
Belof Jonathan L.,
McLaughlin Keith,
Cioce Christian R.,
Tudor Brant,
Hogan Adam,
Laratelli Luciano,
Mulcair Meagan,
Mostrom Matthew,
Navas Alejandro,
Stern Abraham C.,
Forrest Katherine A.,
Pham Tony,
Space Brian
Publication year - 2019
Publication title -
advanced theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.068
H-Index - 17
ISSN - 2513-0390
DOI - 10.1002/adts.201900113
Subject(s) - massively parallel , computer science , monte carlo method , software , molecular dynamics , computational science , supercomputer , polarizability , statistical physics , complex system , parallel computing , physics , artificial intelligence , computational chemistry , chemistry , statistics , mathematics , quantum mechanics , molecule , programming language
Advancements in parallel computing and hardware have allowed computational exploration of chemical systems of interest with unprecendented accuracy and efficiency. The typical development of molecular simulation software is initially inspired by a particular scientific inquiry. The softwares presented herein, MPMC (Massively Parallel Monte Carlo) and MCMD (Monte Carlo/Molecular Dynamics) were born out of a pursuit to simulate condensed phase physical and chemical interactions in porous materials. MPMC first began in 2005 and has been used for dozens of published results in the literature but has not yet been introduced in a standalone paper. MCMD is a more recent expansion and re‐write with some published work, focused on adding molecular dynamics algorithms for transport and other time‐dependent properties of chemical systems. Each software functions as a standalone with some unique and other overlapping features. A driving aim of this work is to consider periodic, long‐range polarization effects in classical simulation, and both codes are optimized to perform such calculations. Sample results will be presented which highlight methodically that inclusion of explicit polarization produces simulation results with predictive power which in general are in greater agreement with experiment than non‐polarizable analogous simulations.