Title:High-Throughput Technologies and Data-Driven Methods Accelerating New Materials Discovery

Speaker:  Shen Bo (City University of Hong Kong)

Invited by: Cui Zhiming (Professor)

Time: May 11, 2026 (Monday), 15:00–17:00

Invited by: Room B8-445, University Town Campus, South China University of Technology

        Biography:

Dr. Shen Bo is a Presidential Assistant Professor in the Department of Materials Science and Engineering at the City University of Hong Kong. He earned his Ph.D. in Chemistry from Brown University in 2019 under the supervision of Professor Shouheng Sun, and subsequently conducted postdoctoral research in the group of Professor Chad A. Mirkin at Northwestern University. Dr. Shen has long been dedicated to high-throughput materials synthesis, the materials genome, and artificial intelligence–assisted materials discovery, with a particular focus on the rapid development of clean-energy-related catalytic materials and the investigation of structure–property relationships. By integrating high-throughput experimentation, data-driven modeling, and machine learning algorithms into a novel materials research and development strategy, he has achieved efficient screening and optimization of multi-component nanomaterials across multi-dimensional parameter spaces encompassing composition, crystal phase, crystal facets, and interface structures. His relevant work has been published as first or corresponding author in international journals including *Nature Synthesis*, *Nature Communications*, *PNAS*, *JACS*, and *Angew*, accumulating more than 3,000 citations. In addition, he has received awards such as the International Institute for Nanotechnology Outstanding Research Award and the Dwight A. Sweigart Inorganic Chemistry Award, and holds two U.S. patents.

Abstract:

The discovery of new materials is a vital driving force for technological progress across numerous fields, playing a key role in microelectronics, pharmaceuticals, energy, and environmental remediation. However, the design and development of new materials typically require the synergistic optimization of multiple parameters, including chemical composition, morphology, size, and crystal structure; traditional empirical trial-and-error approaches can no longer satisfy modern society's demand for efficient research and development. To address this challenge, this presentation introduces a data-driven materials development strategy that combines high-throughput synthesis, high-throughput characterization, and machine learning algorithms to enable rapid screening and performance prediction of complex material systems, thereby significantly enhancing the efficiency of new materials discovery. This methodology has been successfully applied to the controllable preparation of multi-metallic nanomaterials and has demonstrated promising potential in energy conversion systems such as fuel oxidation reactions and carbon dioxide electroreduction. Moreover, this strategy can be further extended to other functional material systems, providing a new research paradigm for the rapid discovery and optimization of high-performance materials.

Announced by School of Chemistry and Chemical Engineering