Professor Dr. Felix Strieth-Kalthoff

Digital Chemistry

 

Research

Synthetic chemistry requires decision making based on numerous factors, such as reactants, reagents, reaction conditions, or procedures. Our lab aims to enhance this decision making process: We develop (Bayesian) machine learning tools for chemical reactivity, leveraging its power to find patterns in complex, high-dimensional data.

Our aim is to create ML tools that are directly useful in a synthetic organic chemistry lab. Importantly, these tools are meant to add to, not replace, the knowledge that expert chemists already have. Examples of such tools include:

• Machine learning for predicting the outcomes of new catalytic reactions
• Optimization algorithms to find and improve reaction conditions
• Data-driven strategies for exploring reaction mechanisms.

Our software tools are developed primarily in Python, making use of established libraries for machine learning and Bayesian optimization (e.g. PyTorch and BoTorch).

Publications