Merck partners with California-based research group to study catalysis innovation through machine learning
Aim is to predict novel catalysts using machine learning (ML) and artificial intelligence (AI) techniques
Merck, a leading science and technology company, today announced a three-year collaboration with the research group of John Hartwig at University of California, Berkeley and Lawrence Berkeley National Laboratory, Berkeley, California. The primary focus of this collaboration between the public research institutes associated with the University of California and the Digital Chemistry Team of Merck will be on understanding the underlying mechanisms for catalysis innovation using machine learning and high-throughput experimentation.
“The application of artificial intelligence to catalysis is a deeply interdisciplinary topic ripe for collaboration. I am excited for the opportunity to assemble the intellect and expertise of researchers at Berkeley and Merck to create new approaches to predict and imagine new catalysts and catalytic reactions for the synthesis of high-value compounds relevant to the Merck portfolio,” said John F. Hartwig, Henry Rapoport Professor of Organic Chemistry at the University of California, Berkeley and Senior Faculty Scientist at Lawrence Berkeley National Laboratory.
“Our commitment to exploring disruptive technologies that have the potential to impact our pioneering businesses is unwavering, and this collaboration on digital design of catalysts represents a significant step forward. I am excited to see what groundbreaking advancements will emerge from this partnership,” said Philipp Harbach, Global Head of Group Digital Innovation of Merck.
The collaboration is funded through the 2022 Merck Research Grant “Chemistry in the cloud – Rapid synthesis through automation”. This annual research grant program was developed in 2018 to explore potentially disruptive technologies that impact the businesses of Merck.
The research of the three partners aims to enhance the process of creating novel catalysis materials by implementing new machine learning representations of catalysts and advanced algorithms to determine the optimal catalysts for a process when given limited reaction data. The machine learning algorithms will be combined with high-throughput experimentation to create optimized predictions using real-world results. Outcomes will include new algorithms and software as well as novel catalysis materials.
John F. Hartwig is the Henry Rapoport Professor of Organic Chemistry at the University of California, Berkeley and Senior Faculty Scientist at Lawrence Berkeley National Laboratory. Hartwig’s research focuses on the discovery and understanding of new reactions catalyzed by transition metal complexes. He is well known for his contributions to widely practiced cross-coupling chemistry that form arylamines, aryl ethers, aryl sulfides, and α-aryl carbonyl compounds as well as for the discovery of practical C-H bond functionalization reactions. He has received numerous awards and was elected to the National Academy of Sciences in 2012. Hartwig was awarded the Emanuel Merck Lectureship in 2022.
The Digital Chemistry Team at Merck concentrates on bringing digital tools and applications to chemistry-related business bottlenecks within the three business sectors of Merck. The group aims to enhance productivity and increase innovation within the current product portfolio of Merck as well as open new opportunities in expanding digital businesses within the organization.
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