SHI Collaboration Profiles

Profile pages for Sustainable Horizons Institute SRP 2025-2026 Project Leaders


Bert de Jong

Bert de Jong

Lawrence Berkeley National Laboratory

Biography

Bert de Jong is the Director of the Quantum Systems Accelerator, which is part of the National Quantum Initiative. In addition, de Jong is the Team Director of the Accelerated Research for Quantum Computing (ARQC) Team MACH-Q, funded by DOE ASCR, focused on developing software stacks for near-term quantum computing devices. In addition, de Jong has a program in AI and machine learning to understand biomolecular processes, and discover new materials and molecular crystals for gas adsorption. de Jong serves as the Department Head for Computational Sciences, and leads the Applied Computing for Scientific Discovery Group, which advances scientific computing by developing and enhancing applications in key disciplines, as well as developing HPC, quantum and AI tools and libraries for addressing general problems in computational science.

SRP Project Title

AI agents for chemical and materials science

Topical Areas

Chemical Engineering; Computer Science; Condensed Matter Physics

Abstract

Discovering new materials or chemical processes is a complex and time-consuming endeavor, often requiring countless experiments, intricate analysis, and sometimes even serendipitous discoveries. Inverse design has the promise to rationally discover new materials and processes. Supported by artificial intelligence, inverse design can dramatically accelerate this process by analyzing vast datasets, predicting material properties, and guiding experimental design—reducing both the time and uncertainty involved in scientific discovery. Our team is working on developing agents, integrated with machine learning and large language models, self-driving experiments and HPC simulations.

Desired Skills

Physical chemistry or materials knowledge would be helpful Good understanding of AI and ML methods, some knowledge of agentic AI.

Lightning Talk Title

AI agents for chemical and materials science

Keywords

AI; ML; chemical processes; materials; discovery; inverse design