Accelerated Discovery of Battery and Alloy Materials via Multiscale Computational Design by Associate Professor Jin Hyun Chang

The traditional trial-and-error approach to materials discovery is increasingly reaching its limits due to the vastness of the chemical design space and the complex, multiscale nature of material behavior. In this seminar, I present an integrated multiscale computational framework designed to bridge the gap between fundamental quantum mechanics and real-world material performance. 

The core of this framework lies in scaling high-fidelity Density Functional Theory (DFT) data through Machine Learning Interatomic Potential (MLIP), Cluster Expansion (CE), and Monte Carlo (MC) simulations. This enables the efficient exploration of the vast configurational space of disordered materials, providing a rigorous thermodynamic and kinetic foundation. By coupling these atomistic insights with Phase-Field Modeling, we can directly simulate microstructural evolution in complex alloys and battery interfaces. This multiscale approach allows us to predict how phases nucleate, grow, and interact under various conditions, ultimately linking atomic-level parameters to macroscopic properties like ionic conductivity and mechanical stability. I will demonstrate the effectiveness of this workflow through case studies on next-generation battery materials and high-performance alloys, highlighting how computational predictions can accelerate the R&D process.