Facilitating the selection and creation of accurate interatomic potentials with robust tools and characterization

Abstract

The Materials Genome Initiative seeks to significantly decrease the cost and time of development of new materials. Within the domain of atomistic simulations, several roadblocks stand in the way of reaching this goal. While the NIST Interatomic Potentials Repository hosts numerous interatomic potentials (force fields), researchers cannot easily determine the best choice for their use case. Researchers developing new potentials, specifically those in restricted environments, lack a comprehensive portfolio of efficient tools capable of calculating and archiving the properties of their potentials. Finally, students, the next generation of materials engineers, have limited access to fully functional and documented examples of production research using atomistic simulations. This paper elucidates one solution to these problems, which uses Python-based frameworks that are suitable for rapid property evaluation and human knowledge transfer.