Simplifying computational workflows with the Multiscale Atomic Zeolite Simulation Environment (MAZE)

Journal Article · Fri Sep 03 00:00:00 UTC 2021 · SoftwareX

DOI: https://doi.org/10.1016/j.softx.2021.100797 · OSTI ID:1981763

Antonio, Dexter D.  ^[1]; Guo, Jiawei ^[1]; Holton, Sam J. ^[1]; Kulkarni, Ambarish R.  ^[1]

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1. University of California, Davis, CA (United States)

Zeolites, an important class of 3-dimensional nanoporous materials, have been widely explored for a variety of applications including gas storage, separations, and catalysis. As the properties of these aluminosilicate materials depend on a number of factors (e.g., framework topology, Si/Al ratio, extra-framework cations etc.), detailed experiments (e.g., catalytic properties, adsorption capacities etc.) are often limited to only a handful of materials. Computational methods have played an important role in (1) providing molecular level insights to rationalize experimental observations, and (2) screening large libraries of zeolites to identify promising candidates for experimental synthesis and validation. Different levels of theory and computational chemistry codes are necessary to describe the range of relevant phenomena such as adsorption (e.g., grand canonical Monte Carlo), diffusion (e.g., molecular dynamics), and chemical reactions (e.g., density functional theory). Manipulation of atomic structures, handling of input files, and developing robust workflows becomes quite cumbersome. To mitigate these challenges, we describe the development of the Multiscale Atomic Zeolite Simulation Environment (MAZE) – a Python package that simplifies zeolite-specific calculation workflows by providing a user-friendly interface for systematically manipulating zeolite structures

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); University of California, Davis, CA (United States)
• Sponsoring Organization: Center for Data Science and Artificial Intelligence Research; Industry–University Cooperative Research Centers (IUCRC); USDOE Office of Science (SC)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1981763
• Journal Information: SoftwareX, Journal Name: SoftwareX Journal Issue: C Vol. 16; ISSN 2352-7110
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English