Automated Identification of Relevant Frontier Orbitals for Chemical Compounds and Processes

Abstract

Quantum-chemical multi-configurational methods are required for a proper description of static electron correlation, a phenomenon inherent to the electronic structure of molecules with multiple (near-)degenerate frontier orbitals. Here, we review how a property of these frontier orbitals, namely the entanglement entropy is related to static electron correlation. A subset of orbitals, the so-called active orbital space, is an essential ingredient for all multi-configurational methods. We proposed an automated selection of this active orbital space, that would otherwise be a tedious and error prone manual procedure, based on entanglement measures. Here, we extend this scheme to demonstrate its capability for the selection of consistent active spaces for several excited states and along reaction coordinates.

8 pages, 4 figures, 1 table

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• Concept of orbital entanglement and correlation in quantum chemistry
• Selected Configuration Interaction in a Basis of Cluster State Tensor Products
• Even-handed subsystem selection in projection-based embedding
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• Molecule-specific Uncertainty Quantification in Quantum Chemical Studies
• Efficient reconstruction of CASCI-type wave functions for a DMRG state using quantum information theory and genetic algorithm
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• Electron Dynamics with the Time-Dependent Density Matrix Renormalization Group
• Explicitly correlated electronic structure calculations with transcorrelated matrix product operators
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• Quantum Proton Effects from Density Matrix Renormalization Group Calculations
• SCINE -- Software for Chemical Interaction Networks
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• Renormalized Internally-Contracted Multireference Coupled Cluster with Perturbative Triples
• QCMaquis 4.0: Multi-Purpose Electronic, Vibrational, and Vibronic Structure and Dynamics Calculations with the Density Matrix Renormalization Group
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• Distinguishing homolytic versus heterolytic bond dissociation of phenyl sulfonium cations with localized active space methods
• Semiclassical Dispersion Corrections efficiently improve Multi-Configurational Theory with Short-Range Density-Functional Dynamic Correlation
• Automatic selection of active spaces for strongly correlated systems using machine learning algorithms