Simulations and machine learning for molecular design and reactivity

The on-site event will be followed by fika in the Analysen coffee area (fika from 16:00-16:30).

Abstract:

Machine learning represents an exciting opportunity to accelerate discovery in the chemical sciences, and to shorten the time from discovery to products. However, the available (experimental) data for chemistry is often limited, and it is not equally distributed in the vast “chemical space”. Our approach is try to bridge this gap by relying on a combination of machine learning and physical simulation. In the first part of the talk, I will describe our work in the field of molecular design for organic electronic materials. Many molecular design algorithms rely on machine learning models to predict the properties of a molecule for a certain application. Although ML models often work well on similar molecules as they were trained on, they often break down when generalizing to different parts of the chemical space. Generative models then abuse these weaknesses of the propery predictor and start generating false positives. We have therefore spent time to develop a series of very fast physics-based property predictors for important properties of organic electronic materials. These can then be coupled with high-throughput virtual screening or molecular design models to discovery new promising candidates. An alternative is to work in a constrained fragment space, which ensures that machine learning methods are sufficiently generalizable. We will give examples for such as fragment-constrained optimization of singlet fission materials using genetic algorithms which are steered by prediction uncertainty. In the second part of the talk, I will present our work in the area of reaction prediction, using a combination of quantum-chemical models and machine learning. Also here, we have developed fast physics-based property predictors for chemical reactivity that we use in generative models, including the first benchmark task for chemical reaction design. Using these simulations methods, we also generate large reactivity datasets on which deep learning models can be trained.

About the speaker:

Kjell Jorner is an Assistant Professor of Digital Chemistry at ETH Zurich since 2023. His work focuses on accelerating chemical discovery with digital tools, with a special emphasis on reactivity and catalysis. His group does interdisciplinary research, drawing from the fields of computational chemistry, cheminformatics and machine learning. Before joining ETH Zurich, he was a postdoctoral researcher with Alán Aspuru-Guzik (2021-2022) and at AstraZenecaUK (2018-2020). Kjell has a PhD from Uppsala University (2018) on computational physical organic chemistry for the photochemistry of aromatic compounds.