The field of computational chemistry increasingly relies on automated data-driven pipelines designed to facilitate and accelerate the discovery of molecules and materials with tailored properties (i.e., inverse design pipelines). These efforts require extensive infrastructure, integrating quantum chemistry, statistical models, data curation and software to enable the practical discovery of previously unknown molecules. In this talk, I will present our efforts in this direction highlighting several key aspects and providing practical examples. Specifically, I will discuss how we have extensively mined crystallographic repositories to generate fragment-based curated databases such as OSCAR (>1M organocatalysts) [1] and FORMED (>100k organic molecules and excited state properties). [2] Additionally I will elaborate on the complex fitness functions [3] and machine learning models [4] we have designed to guide optimization tasks. Beginning with high-throughput virtual screening, we have progressively developed more sophisticated inverse design workflows, each with its own advantages and limitations. I will demonstrate how we have applied these workflows to the field of homogeneous catalysis (e.g., frustrated Lewis pairs for CO 2 hydrogenation) [5] and organic functional materials. singlet fission molecular chromophores.
Biography:
Clemence Corminboeuf is currently professor at the Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland. She earned her Ph.D. degrees from the University of Geneva and held postdoctoral positions at New York University and the University of Georgia. She began her independent career in 2008 at the EPFL as an assistant professor and Sandoz Family Foundation Chair. She is the recipient of the silver medal at the European Young Chemist Award (2010), the Werner Prize of the Swiss Chemical Society (2014), and the Theoretical Chemistry Award from the American Chemical Society Physical Chemistry Division (2018). In 2021, she received the Heilbronner-Huckel Lecture Award from the Swiss and German chemical society. In 2022 she was the awardee of the Almlöf–Gropen and the Löwdin lectures. She also received two ERC grants (starting in 2012 and consolidator in 2018) and is associate editor of the Journal of Chemical Theory and Computation since 2021. Clemence’s research focuses on electronic structure theory using the interplay of deterministic and statistical approaches. These approaches are exploited in conceptual work and applications in the area of homogeneous catalysts and molecular organic materials. Her group for instance introduced computational workflows and concepts for the discovery of homogenous catalysis along with physics-based statistical models to predict molecular and reaction properties.
