Deep Forest Sciences, in collaboration with the University of Michigan, received a DOE INCITE Award that provides access to 200,000 Polaris node hours to advance research on foundation models for inorganic materials.

This project, led by Venkat Viswanathan, focuses on using large language models (LLMs) to accelerate the design of battery electrolytes. The effort builds on the CLOUD representation for crystalline materials led by Changwen Xu, as well as earlier innovations like ChemBERTa, introduced by Dr. Bharath Ramsundar. Supported by the Michigan Institute for Computational Discovery & Engineering (MICDE) and Argonne’s cutting-edge Polaris supercomputer, the team will train models on massive datasets, scaling up to 50 billion molecules to uncover new scientific insights. These advanced models aim to bridge the gap between computational predictions and experimental validation, enabling rapid testing of promising compounds.

To read more about the project, visit the ASCR article featured by the U.S. Department of Energy Office of Science.

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