Dr. Jan Gerit Brandenburg (Director for Digital Chemistry Merck)

Abstract: Advances in machine learning (ML) and digital technologies are transforming how we design, simulate, and optimize molecules and materials. Yet scaling computational workflows from academic prototypes to robust, industrial-grade solutions remains a central challenge. In this lecture, Jan Gerit Brandenburg will outline how ML-assisted computational chemistry can be built into scalable workflows that accelerate discovery and development across domains such as materials, life sciences, and healthcare. Drawing on experience from academia, industry, and entrepreneurship, he will illustrate how integrating quantum mechanics, molecular dynamics, data-driven models, and cloud-native infrastructures enables new levels of speed, accuracy, and automation. Case studies will highlight successful applications—from impurity detection in OLED production to fully self-driving flow chemistry labs—showcasing how digital chemistry delivers tangible innovation at scale.