In collaboration with the U.S. Naval Nuclear Laboratory, this project focuses on developing AI based tools to accelerate the discovery of radiation-resistant structural alloys. Materials deployed in nuclear environments are exposed to intense radiation fields that can induce lattice defects, embrittlement, and long-term degradation, making the identification of resilient materials a critical challenge.

Traditional experimental screening of candidate alloys is both time-consuming and costly. To address this, the WISER research team is building a computational pipeline with lightweight and resource-efficient predictive models that can operate with modest computational requirements while maintaining strong predictive performance. The framework is designed to be adaptable, enabling researchers to optimize materials for multiple target properties including radiation tolerance, hardness, ductility, and mechanical stability.

The project is currently in progress and aims to provide a flexible AI-assisted materials design platform capable of guiding experimental efforts and reducing the need for extensive trial-and-error testing in the development of advanced structural alloys.

WISER Research Fellows: Allison Nicole Arber, Miles Miller-Dickson, Arun Moorthy, Maham Khalid