MoSDeF Cassandra: A complete Python interface for the Cassandra Monte Carlo software

• Published in: Journal of computational chemistry Vol. 42; no. 18; pp. 1321 - 1331
• Main Authors: DeFever, Ryan S., Matsumoto, Ray A., Dowling, Alexander W., Cummings, Peter T., Maginn, Edward J.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 05.07.2021; Wiley Subscription Services, Inc
• Subjects: Graphene; Interoperability; Molecular dynamics; molecular modeling; Monte Carlo; open source software; Porous media; Python; reproducible research; Simulation; Software; thermodynamics; Workflow; thermodynamics; Monte Carlo; open source software; molecular modeling; reproducible research
• ISSN: 0192-8651; 1096-987X
• DOI: 10.1002/jcc.26544

We introduce a new Python interface for the Cassandra Monte Carlo software, molecular simulation design framework (MoSDeF) Cassandra. MoSDeF Cassandra provides a simplified user interface, offers broader interoperability with other molecular simulation codes, enables the construction of programmatic and reproducible molecular simulation workflows, and builds the infrastructure necessary for high‐throughput Monte Carlo studies. Many of the capabilities of MoSDeF Cassandra are enabled via tight integration with MoSDeF. We discuss the motivation and design of MoSDeF Cassandra and proceed to demonstrate both simple use‐cases and more complex workflows, including adsorption in porous media and a combined molecular dynamics – Monte Carlo workflow for computing lateral diffusivity in graphene slit pores. The examples presented herein demonstrate how even relatively complex simulation workflows can be reduced to, at most, a few files of Python code that can be version‐controlled and shared with other researchers. We believe this paradigm will enable more rapid research advances and represents the future of molecular simulations. MoSDeF Cassandra is a Python interface to the Cassandra Monte Carlo software that enables the development of function‐driven molecular simulation workflows for reproducible science.