Bayesian optimization with known experimental and design constraints for chemistry applications

Abstract

A Bayesian optimization algorithm that satisfies known constraints has been developed. The usefulness of considering experimental and design constraints are shown in two simulated chemistry applications.

Document Details

• Document Type: Pub Defense Publication
• Publication Date: Jan 01, 2022
• Source ID: 10.1039/d2dd00028h

Entities

People

• Alán Aspuru-Guzik
• Florian Häse
• Matteo Aldeghi
• Riley J. Hickman

Organizations

• Canadian Institute for Advanced Research
• Harvard University
• Massachusetts Institute of Technology
• Natural Sciences and Engineering Research Council
• Office of Naval Research
• University of Toronto
• Vector Institute

Tags

Readers

• Nanoscale Plasmonic Nanotechnology
• Parallel and Distributed Computing.
• Statistical inference.

Technology Areas

• AI & ML
• AI & ML - Bayesian Inference
• AI & ML - Machine Learning Algorithms