Affiliation:
1. Department of Chemical Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge Massachusetts 02139 United States
2. Ecole Nationale Supérieure de Chimie de Paris Université PSL, CNRS Institute of Chemistry for Life and Health Sciences 75005 Paris France
3. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge Massachusetts 02139 United States
Abstract
AbstractMolecular quantum mechanical modeling, accelerated by machine learning, has opened the door to high‐throughput screening campaigns of complex properties, such as the activation energies of chemical reactions and absorption/emission spectra of materials and molecules; in silico. Here, we present an overview of the main principles, concepts, and design considerations involved in such hybrid computational quantum chemistry/machine learning screening workflows, with a special emphasis on some recent examples of their successful application. We end with a brief outlook of further advances that will benefit the field.
Subject
General Chemistry,Catalysis,Organic Chemistry