Computational Modeling of Molecular Mechanics for the Experimentally Inclined
AT Kleinschmidt and AX Chen and TA Pascal and DJ Lipomi, CHEMISTRY OF MATERIALS, 34, 7620-7634 (2022).
Modern computer simulations can provide unique atomic-scale insights into complex material systems, but the method of performing a simulation may seem obscure to the nonspecialist. The goal of this Protocol is to introduce to experimental researchers a description of the tools and methods used in atomic simulations which elucidate the structure, morphology, and dynamics of polymers and nanomaterials. In particular, it focuses on the workflow and logistics of simulations in which the central component is indivisible atoms ("atomistic" as opposed to "quantum" or "continuum" methods). We present methods which describe the positions of atoms, e.g., Monte Carlo (MC) and molecular dynamics (MD) simulations, along with the necessary processes by which simulations are set up, run, and analyzed. However, much of the terminology and workflow outlined in this Protocol is general and thus applies to methods beyond MC and MD as well as other molecular systems (e.g., proteins). This Protocol is separated into three general sections. First, it describes the three types of information that are required for a simulation: a description of the system (i.e., "data file"), instructions for the simulations engine (i.e., "software input file"), and instructions for the hardware, usually supercomputing infrastructure (i.e., "hardware input file"). The data file, generally, describes the initial state of the system as well as a definition of how the atoms within the system interact (usually denoted as a "force field"). Together, these three pieces of information are used to run a simulation, which then produces an output that can be analyzed by the researcher. We hope that this Protocol will provide at least three things for the experimentalist: (1) a frame of reference for the interpretation of computational data, (2) facilitation of collaboration with computational scientists, and (3) the encouragement to perform some computational tasks on their own.
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