Collaborative UMN research team awarded $1.13 million from NSF
A collaborative research team from the University of Minnesota, including Chemical Engineering and Materials Science (CEMS) Assistant Professor Stefano Martiniani (a member of the data science graduate faculty), and Professors Ellad Tadmor and Ryan S. Elliott from the Department of Aerospace Engineering and Mechanics (AEM), was awarded a $1.13 million from the National Science Foundation (NSF) for their project "Data CI Pilot: CI-Based Collaborative Development of Data-Driven Interatomic Potentials for Predictive Molecular Simulations."
The emergence of data-driven approaches for developing interatomic potentials promises to transform materials design and synthesis. Data-driven interatomic potentials (DDIPs) build on recent advances in machine learning to accurately model the potential energy surface of a material system by inferring its underlying functional form from a large number of quantum input configurations. DDIPs thus enable truly predictive molecular simulations with the accuracy of first principle methods over length and time scales comparable to classical molecular simulations.
This project aims to create a computational framework “ColabFit” that enables researchers to rapidly develop and deploy DDIPs for complex material systems by connecting existing cyberinfrastructure resources of first principles and experimental data with a variety of fitting frameworks. Building on an interoperable standard for machine learning models, researchers using ColabFit will be able to archive their state-of-the-art DDIPs and training sets to the Open Knowledgebase of Interatomic Models (OpenKIM) project, and retrieve existing ones to continue their collaborative development within a supported fitting framework of their choosing. Integration with OpenKIM will ensure that any DDIP created with ColabFit can be immediately used in multiple major simulation packages. ColabFit will be developed in collaboration with an international consortium of leaders in DDIP development, high-throughput first principles computation cyberinfrastructures, and materials standards organizations. The project will be tested on a target application of DDIP development for phase transformations in 2D transition metal dichalcogenides.
This project addresses a pressing need of the molecular simulation community. The creation of ColabFit will provide materials researchers with a powerful new ability to efficiently synthesize all available data and knowledge related to their particular problem of study. DDIPs developed through ColabFit and shared through OpenKIM will be archived with full provenance and version control and a persistent digital object identifier (DOI) to enable reproducible science and R&D, and be available to other researchers in the community to build upon by extending them for their own needs. Thus, major inefficiencies in today’s materials research industry will be eliminated and all of society can benefit from the resulting increase in scientific advancement.