SI2-SSE: Development of Cassandra, A General, Efficient and Parallel Monte Carlo Multiscale Modeling Software Platform for Materials Research

University Of Notre Dame, Notre Dame IN

Investigators

Abstract

The properties of materials are the result of the interactions between the atoms that make up these materials. These properties can now be predicted with great accuracy, even for materials that have not yet existed in nature, by using advanced computational methods to study how the constituent atoms of the materials interact with one another and their environment. This field relies upon the existence of sophisticated software packages that enable researchers to conduct these simulations. There are two general approaches for simulating bulk materials: molecular dynamics and Monte Carlo, each of which is appropriate for certain problems. There are many molecular dynamics software packages available but almost no general purpose Monte Carlo codes. This project seeks to develop an efficient, general-purpose open source Monte Carlo code called Cassandra. To do this, the academic Monte Carlo code developed in the PI's group will be extended and enhanced. The code will be capable of simulating any type of molecule in bulk and heterogeneous environments. The code will contain a wide range of advanced features, making it useful for a range of problems. By providing a general purpose code to the research community and establishing a mechanism whereby users can add their own features and extend the code, this project will have a broad impact on the research community. It will enable non-experts to use Monte Carlo simulations to study new problems. It will enable experienced molecular modelers to utilize and contribute features to a single optimized and validated code, thereby alleviating the time and expense associated with developing specialized codes for individual applications. Because the code will be used in teaching and workshops, materials will be made available to educators to use the code in the classroom when teaching courses such as thermodynamics, molecular modeling and statistical mechanics.