Zhuotao Jin

Zhuotao Jin is a graduate student in computational science and engineering and materials science and engineering working on machine learning exchange-correlation functionals for density functional theory. His work addresses a critical computational bottleneck: achieving hybrid-functional accuracy at generalized gradient approximation (GGA) cost, enabling high-throughput screening of thousands of materials. He developed a machine learning functional that delivers hybrid-functional accuracy for battery materials, transition metal oxides, and molecular reactions, supported by MATLAB toolboxes for optimization, statistical analysis, and machine learning. For systematic validation across material classes, he built MATLAB-based benchmarking pipelines and is developing new non-local XC functionals. As a MathWorks Fellow, Jin will scale these approaches to higher-dimensional systems and open-source MATLAB codebases for the materials science community. His work could open new pathways for democratizing quantum mechanical simulations, making rigorous quantum calculations accessible to researchers without specialized computing resources.