Machine learning in theoretical and computational chemistry

Contact person: Thomas Bondo Pedersen
Keywords: Theoretical chemistry, machine learning, partial differential equations, curse of dimensions, data scarcity
Research group: Hylleraas Centre for Quantum Molecular Sciences
Department of Chemistry

Chemistry plays a crucial role in addressing several significant global challenges. Achieving the green transition, discovering life-saving drugs, and developing energy-saving and environmentally friendly materials all heavily rely on advancements in theoretical and computational chemistry. The realm of theoretical chemistry encompasses two primary aspects: the development of efficient computational tools and the utilization of those tools for chemical discovery and analysis. However, this field faces obstacles that can be described as the dual curse of dimensions, resulting from the intricacies of many-body dynamics and the vastness of chemical space.

The advent of machine learning and data-driven science is revolutionising computational and theoretical chemistry, enabling remarkable progress in all fields of chemistry. However, the field encounters a distinctive combination of challenges encompassing complexity and a severe lack of available data. Consequently, theoretical chemistry presents unparalleled opportunities for the advancement of methodologies in machine learning and computer science.

Research topics:

Deep learning and genetic algorithms for the inverse design of catalysts in the field of green chemistry
Machine learning for numerical optimisation problems in quantum chemistry and quantum dynamics
Machine learning force-fields for atomistic and coarse-grained molecular dynamics
Application of symbolic regression and genetic algorithms for discovery of fast and accurate density-functional approximations

Research team:

Mentoring and internship will be offered by a relevant external partner.