SNIC SUPR
Artificiall Intelligence in quantum chemistry wave function and molecular optimization
Dnr:

SNIC 2019/5-163

Type:

SNIC Small Compute

Principal Investigator:

Gerardo Raggi

Affiliation:

Uppsala universitet

Start Date:

2019-11-26

End Date:

2020-12-01

Primary Classification:

10407: Theoretical Chemistry

Webpage:

Allocation

Abstract

To introduce machine learning technique for surrogate models in quantum chemistry simulations with the particular target of improved techniques for wave function and molecular geometry optimizations. The success of the project will ina fundamental way change how computational chemistry implemented and executed their optimization procedures.