Ericmoore Jossou is an Assistant Professor in the Nuclear Science & Engineering and Electrical Engineering and Computer Science Departments at MIT, where he leads the Materials in Extreme Environments Group. Before moving to MIT, he worked at Brookhaven National Laboratory. He received his PhD in Mechanical Engineering with concentration in Materials Science in 2019 from the University of Saskatchewan. He has a BSc in Chemistry from the Ahmadu Bello University, and MSc in Materials Science and Engineering from the African University of Science and Technology in 2013. His research interest is in materials design for nuclear energy applications. He currently leads the materials in extreme environment research group, which combines experiments with computational methods to establish structure-properties-performance relationships in materials for nuclear energy applications.
High-resolution three- and four-dimensional imaging of micro- and nanostructural features enables the development of high-fidelity qualitative and quantitative structure–property relationships in materials. Our group integrates electron microscopy with scanning probe X-ray techniques, including Bragg Coherent Diffraction Imaging and Bragg Ptychography, to monitor material degradation processes at the nanoscale. By coupling these advanced imaging methods with state-of-the-art machine learning algorithms, we gain fundamental insights into failure mechanisms in materials exposed to nuclear reactor–relevant environments such as irradiation, corrosion, hydriding conditions, elevated temperatures, and mechanical stress.
Traditional materials design relies mainly on manually characterizing physical and chemical properties to select candidate materials. As such, new materials design is time consuming and relies heavily on intuition, trial, and error. To ramp up the materials design process, we are coupling materials synthesis with high throughput characterization and materials informatics to make these decisions autonomously. Therefore, with these new tools, we can rationally narrow down a finite multi-element phase space to explore in detail. This approach will facilitate rapid alloy design with tailored properties for specific applications.
Actinide compounds such as UO2, U-Zr, U3Si2, etc., are materials used as fuel in nuclear reactors. Understanding them at the atomic scale is critical for optimal nuclear fuel design. However, due to the complexity of describing the interactions of the 5f electrons, first principles-based methods such as density functional theory (DFT) for investigations of actinides are difficult and computationally expensive. To tackle this problem, we will develop machine-learning based interatomic potential with the accuracy of DFT to study the thermophysical, interfacial, and radiation properties of actinide systems.
Self-organization of nanostructures such as gas bubbles, voids, and precipitates into superstructures is of great scientific and technological importance. Such ordering alters the potential energy surface, which has implications for the migration of defects, ions, and fission gases. This project is focused on understanding the superstructure-properties relationship to inform the design of radiation tolerant and cyclic process driven materials.
22.01. Introduction to Nuclear Engineering and Ionizing Radiation
22.C01/C51. Modeling with Machine Learning: Nuclear Science and Engineering Applications