Research Overview
Our lab leverages machine learning and high-performance computing simulations to accelerate the discovery and understanding of new materials. We focus on the following key research thrusts:
Machine Learning and Electronic Structure Methods
We develop the Materials Learning Algorithms (MALA), a physics-informed machine learning framework designed to accelerate conventional density functional theory simulations. MALA employs neural network architectures to accurately predict electronic structures across different parameter spaces. Our scalable approach overcomes the limitations of conventional density functional theory simulations, paving the way for electronic structure calculations at unprecedented length and time scales.
Key Areas: Neural Networks, Electronic Structure Theory, Density Functional Theory, Atomistic Simulations
Atomistic Molecular Spin Dynamics
We combine first-principles calculations with machine learning models to generate accurate interatomic potentials for high-performance molecular-spin dynamics simulations. Our computational framework allow simultaneous simulations of ionic and spin dynamics, enabling detailed analyses of structural stability, transport phenomena, and magneto-structural phase transitions. This approach holds promise for developing next-generation magnetic materials and ultrafast magnetic storage technologies.
Key Areas: Machine Learning Interatomic Potentials, Atomistic Simulations, Molecular Dynamics, Spin Dynamics, Magnetic Materials
Explorative Artificial Intelligence for Materials Modeling
We leverage state-of-the-art machine learning techniques to advance and automate first-principles simulations, paving the way for rapid and targeted materials discovery. We employ physics-informed neural networks for inverting fundamental quantum mechanical equations, neural operators for modeling electron dynamics, and generative models for materials discovery.
Key Areas: Physics-Informed Neural Networks, Neural Operators, Time-Dependent Density Functional Theory, Exchange-Correlation Functionals, AI for Materials Discovery