Name: Bert de Jong
Pronouns: he/him/his
Biography:
Bert de Jong leads the Applied Computing for Scientific Discovery Group, which advances scientific computing by developing and enhancing applications in key disciplines, as well as developing tools and libraries for addressing general problems in computational science. He currently serves as the Department Head for Computational Sciences. de Jong is the deputy director of the Quantum Systems Accelerator, which is part of the National Quantum Initiative. In addition, de Jong directs the AIDE-QC focused on developing software stacks, algorithms, and computer science and applied mathematics solutions for chemical sciences and other fields on near-term quantum computing devices. de Jong is a co-PI within the DOE ASCR Exascale Computing Project (ECP) as the LBNL lead for the NWChemEx effort, contributing to the development of an exascale computational chemistry code. He is the LBNL lead for the Basic Energy Sciences SPEC Computational Chemistry Center (led out of PNNL), where he is working on reduced scaling MCSCF and beyond GW approaches for molecules. de Jong works on Rare Earth and carbon capture from air projects, where he focuses on using machine learning and computational chemistry to discover new materials for rare earth separation, and designing new molecular crystals for carbon dioxide adsorption.
Institution/Lab: Lawrence Berkeley National Laboratory
Website: https://crd.lbl.gov/divisions/amcr/computational-science-dept/acsd/staff/staff-members/bert-de-jong/
SRP Collaboration Topic/Title: Enabling scientific discovery with HPC, AI and Quantum
Field or research area: AI and Quantum
Please select all the topical areas that apply to your project:
Computational Science Applications (i.e., bioscience, cosmology, chemistry, environmental science, nanotechnology, climate, etc.); High-Performance Computing; Machine Learning and AI; Quantum Computing and Information Science
Brief Abstract:
One of the big challenges in finding new molecules that can better capture solar energy, to design molecular crystals that can better capture CO2 from the air, or materials that can convert heat into electricity is the very large search space of possibilities. We can accelerate this search with AI and machine learning methods that allow us to quickly determine if a molecule or material has the desired properties, and if it can be made, but this search can still take forever. We have been developing approaches for inverse design. Projects can involve the further development of our inverse design methods, or exploring our tools for real science questions. Quantum computing has received a lot of attention. We now have noisy quantum computers online on which we can do actual experiments. Our projects range from developing algorithms and simulation approaches that translate scientific problems we run on classical computers to problems that can be run on quantum computers…and we run them on quantum computers to get scientific results.
Desired relevant skills, background, or interests:
For AI related research, some understanding of machine learning approaches and some cursory reading of the literature with respect to inverse design for chemistry and materials. For quantum related research, a basic understanding of quantum computing would be a benefit. Reading one of the popular quantum computing books, for example the first parts of Nielsen and Chuang (Quantum Computation and Quantum Information), and doing some of the online qiskit tutorials will give you a great head start.
Other comments:
Do any special requirements apply? In-Person Only
Other, specify:
Keywords:
Chemistry; AI; ML; HPC; quantum computing
Lightning Talk Title: Solving chemistry problems with exascale, AI and quantum computing