High-throughput ab initio reaction mechanism exploration in the cloud with automated multi-reference validation

• Published in: The Journal of chemical physics Vol. 158; no. 8; pp. 084803 - 84815
• Main Authors: Unsleber, Jan P., Liu, Hongbin, Talirz, Leopold, Weymuth, Thomas, Mörchen, Maximilian, Grofe, Adam, Wecker, Dave, Stein, Christopher J., Panyala, Ajay, Peng, Bo, Kowalski, Karol, Troyer, Matthias, Reiher, Markus
• Format: Journal Article
• Language: English
• Published: United States American Institute of Physics 28.02.2023; American Institute of Physics (AIP)
• Subjects: Automation; Autonomy; catalysis; catalysts; Cloud computing; Clusters; computational chemistry; coupled-cluster methods; Coupling (molecular); Density functional theory; electron correlation; Electronic structure; electronic structure methods; electronic structure theory; high performance computing; hydrogenation process; INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Ketones; Mathematical analysis; Molecular structure; Physics; quantum chemical calculations; Quantum chemistry; Reaction mechanisms; transition state; Workflow
• ISSN: 0021-9606; 1089-7690; 1089-7690
• DOI: 10.1063/5.0136526

Quantum chemical calculations on atomistic systems have evolved into a standard approach to studying molecular matter. These calculations often involve a significant amount of manual input and expertise, although most of this effort could be automated, which would alleviate the need for expertise in software and hardware accessibility. Here, we present the AutoRXN workflow, an automated workflow for exploratory high-throughput electronic structure calculations of molecular systems, in which (i) density functional theory methods are exploited to deliver minimum and transition-state structures and corresponding energies and properties, (ii) coupled cluster calculations are then launched for optimized structures to provide more accurate energy and property estimates, and (iii) multi-reference diagnostics are evaluated to back check the coupled cluster results and subject them to automated multi-configurational calculations for potential multi-configurational cases. All calculations are carried out in a cloud environment and support massive computational campaigns. Key features of all components of the AutoRXN workflow are autonomy, stability, and minimum operator interference. We highlight the AutoRXN workflow with the example of an autonomous reaction mechanism exploration of the mode of action of a homogeneous catalyst for the asymmetric reduction of ketones.