Towards Practical Experiment Planning with Machine Learning
Veranstaltungen
15:15 – 16:15
Hannover
Institut für Organische Chemie, Schneiderberg 1B, 30167, Hannover, Deutschland
Prof. Dr. Felix Strieth-Kalthoff
Bergische Universität Wuppertal
Chemistry and materials science regularly involve decision-making tasks of varying complexity – from selecting which material to synthesize and test, to choosing reaction conditions, to configuring instrument parameters. These problems are often high-dimensional and nonlinear, suggesting they could be addressed using machine learning (ML). Over the last decade, Bayesian ML has been widely established for effective decisionmaking under uncertainty, and has gained traction in chemistry in recent years. In this talk, I will discuss some of our recent efforts to incorporate Bayesian ML tools into experimental workflows. Using case studies from synthetic chemistry and functional molecule discovery, the talk will highlight the opportunities and challenges in ML to support laboratory decision making.
Kontaktperson
Prof.Dr. Jens-Uwe Grabow
Zu Ihrem Kalender hinzufügen
Interessen
Veranstaltungsdetails
15:15 – 16:15
Hannover
Institut für Organische Chemie, Schneiderberg 1B, 30167, Hannover, Deutschland
Prof. Dr. Felix Strieth-Kalthoff
Bergische Universität Wuppertal
Kontaktperson
Prof.Dr. Jens-Uwe Grabow
Zu Ihrem Kalender hinzufügen
