Interactive Quantum Chemistry Enabled by Machine Learning, Graphical Processing Units, and Cloud Computing

Author:

Raucci Umberto123,Weir Hayley12,Sakshuwong Sukolsak4,Seritan Stefan125,Hicks Colton B.12,Vannucci Fabio6,Rea Francesco6,Martínez Todd J.12

Affiliation:

1. Stanford PULSE Institute, SLAC National Accelerator Laboratory, Menlo Park, California, USA

2. Department of Chemistry, Stanford University, Stanford, California, USA;

3. Current affiliation: Italian Institute of Technology, Genova, Italy

4. Department of Management Science and Engineering, Stanford University, Stanford, California, USA

5. Current affiliation: Sandia National Laboratories, Livermore, California, USA

6. Italian Institute of Technology, Genova, Italy

Abstract

Modern quantum chemistry algorithms are increasingly able to accurately predict molecular properties that are useful for chemists in research and education. Despite this progress, performing such calculations is currently unattainable to the wider chemistry community, as they often require domain expertise, computer programming skills, and powerful computer hardware. In this review, we outline methods to eliminate these barriers using cutting-edge technologies. We discuss the ingredients needed to create accessible platforms that can compute quantum chemistry properties in real time, including graphical processing units–accelerated quantum chemistry in the cloud, artificial intelligence–driven natural molecule input methods, and extended reality visualization. We end by highlighting a series of exciting applications that assemble these components to create uniquely interactive platforms for computing and visualizing spectra, 3D structures, molecular orbitals, and many other chemical properties.

Publisher

Annual Reviews

Subject

Physical and Theoretical Chemistry

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