The Camille and Henry Dreyfus Foundation announces eight award recipients of the 2022 program for Machine Learning in the Chemical Sciences and Engineering, totaling $800,019. The Foundation anticipates that these projects will contribute new fundamental chemical insight and innovation in the field.

Connor G. Bischak, University of Utah
Uncovering Structure-Function Relationships in Organic Mixed Conductors: High-Throughput Electrochemistry Guided by Machine Learning

Stephen Leffler Buchwald, Massachusetts Institute of Technology
Active Scientific Machine Learning of Chemical Reaction Networks and Chemical Reactivity in Transition Metal Catalysis

Robert A. DiStasio Jr., Cornell University
Semi-Local Density Fingerprints for Machine Learning Molecular Properties, Intra-/Inter-Molecular Interactions, and Chemical Reactions

Julia Dshemuchadse, Cornell University
Discovery of New Self-Assembled Crystal Structures for Materials Design

Boris Kozinsky, Harvard University
Learning Non-Local Functionals and Equivariant Models for Reactive Dynamics

Wenhao Sun, University of Michigan
Machine-Learning Classification of Materials Synthesizability

Mark E. Tuckerman, New York University
Machine Learning the Theorems of Density Functional Theory

Gregory A. Voth, University of Chicago
Physics-Constrained Machine Learning for Reactive Molecular Dynamics