The Camille and Henry Dreyfus Foundation announces eight award recipients of the inaugural program for Machine Learning in the Chemical Sciences and Engineering, totaling $789,722. The Foundation anticipates that these projects will contribute new fundamental chemical insight and innovation in the field.

2020 Machine Learning in the Chemical Sciences & Engineering Awards:

Frances Arnold, California Institute of Technology
Validation and Dissemination of Machine Learning-Assisted Enzyme Engineering

 

Andrew Ferguson, The University of Chicago
Data-driven Protein Engineering Using Deep Generative Learning and High-throughput Gene Synthesis

 

Jason Goodpaster, University of Minnesota
Machine Learning Models for Chemical Reactions

 

Klavs Jensen, Massachusetts Institute of Technology
Machine-Learning-Guided Discovery of New Electrochemical Reactions

 

Yu-Shan Lin, Tufts University
Low-supervision Machine Learning for Automated Analysis of Molecular Dynamics Simulations

 

Thomas Miller, California Institute of Technology
Molecular-Orbital-Based Machine Learning for Excited States

 

Brett Savoie, Purdue University
Transfer Learning for Deep Generative Chemical Models

 

John Seinfeld, California Institute of Technology
Application of Machine Learning to Represent the Molecular Routes Comprising Atmospheric Chemistry