Hao Li

Associate Professor
Tohoku University
Advanced Institute for Materials Research

The Progress of “Turing Plan” for AI-Driven Materials Design

Abstract

This talk will display the progress of the “Materials Turing Plan” led by the Hao Li Lab at WPI-AIMR, for materials design. We will show an avenue to realize an AI-driven framework for materials design combining digital platform development, large-scale data mining, predictive modeling, computational methodology development, and experiments. In particular, we will discuss: i) our Digital Catalysis Platform (DigCat; the first full heterogeneous electrocatalysis database to date) developed via large-scale data mining from experiments and large language model training, ii) how to reduce the complexity in materials design by materials theory, in particular for hydrogen storage and hydrogen catalysis, and iii) how to develop new computational methods (i.e., package, on-the-cloud platform, model, and algorithm) to accelerate materials simulation. This talk will show the predictive power of theory in electrochemical and thermal catalysis, solid-state battery electrolytes, and hydrogen storage materials. Finally, we will discuss the practical design of materials fusing AI, materials theory, computational screening, computational methodology development, and experiments.

Bio

Hao Li is an Associate Professor at the Advanced Institute for Materials Research of Tohoku University, Japan. He developed the “Digital Catalysis and Battery Lab (DigCat & DigBat)” as the Principal Investigator in 2022. He is also the Adjunct Investigator of the ARC Centre of Excellence for Green Electrochemical Transformation of Carbon Dioxide (GETCO2), The University of Queensland, Australia. Before his independent career, he obtained his Ph.D. degree at The University of Texas at Austin in 2019. He was a postdoc researcher at Technical University of Denmark, in the group of Prof. Jens K. Nørskov.

Hao’s research interests consist of i) materials and catalysis theory, ii) computational methodology based upon AI, and iii) design of cost-effective catalysts, batteries, and hydrogen storage materials with special emphasis on sustainable production.