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Organic Reactivity Made Easy and Accurate with Automated Multireference Calculations

Jacob J. Wardzala, Daniel S. King, Lawal Adewale Ogunfowora, Brett M. Savoie, Laura Gagliardi

2024ACS Central Science13 citationsDOIOpen Access PDF

Abstract

In organic reactivity studies, quantum chemical calculations play a pivotal role as the foundation of understanding and machine learning model development. While prevalent black-box methods like density functional theory (DFT) and coupled-cluster theory (e.g., CCSD(T)) have significantly advanced our understanding of chemical reactivity, they frequently fall short in describing multiconfigurational transition states and intermediates. Achieving a more accurate description necessitates the use of multireference methods. However, these methods have not been used at scale due to their often-faulty predictions without expert input. Here, we overcome this deficiency with automated multiconfigurational pair-density functional theory (MC-PDFT) calculations. We apply this method to 908 automatically generated organic reactions. We find 68% of these reactions present significant multiconfigurational character in which the automated multiconfigurational approach often provides a more accurate and/or efficient description than DFT and CCSD(T). This work presents the first high-throughput application of automated multiconfigurational methods to reactivity, enabled by automated active space selection algorithms and the computation of electronic correlation with MC-PDFT on-top functionals. This approach can be used in a black-box fashion, avoiding significant active space inconsistency error in both single- and multireference cases and providing accurate multiconfigurational descriptions when needed.

Topics & Concepts

Chemical spaceCoupled clusterReactivity (psychology)Density functional theoryComputer scienceComputational chemistrySpace (punctuation)Statistical physicsChemistryPhysicsQuantum mechanicsDrug discoveryMoleculePathologyBiochemistryOperating systemAlternative medicineMedicineMachine Learning in Materials ScienceComputational Drug Discovery MethodsCatalysis and Oxidation Reactions
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