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Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation
journal contribution
posted on 2020-03-12, 15:03 authored by WooSeok Jeong, Samuel J. Stoneburner, Daniel King, Ruye Li, Andrew Walker, Roland Lindh, Laura GagliardiPredicting
and understanding the chemical bond is one of the major
challenges of computational quantum chemistry. Kohn–Sham density
functional theory (KS-DFT) is the most common method, but approximate
density functionals may not be able to describe systems where multiple
electronic configurations are equally important. Multiconfigurational
wave functions, on the other hand, can provide a detailed understanding
of the electronic structures and chemical bonds of such systems. In
the complete active space self-consistent field (CASSCF) method, one
performs a full configuration interaction calculation in an active
space consisting of active electrons and active orbitals. However,
CASSCF and its variants require the selection of these active spaces.
This choice is not black box; it requires significant experience and
testing by the user, and thus active space methods are not considered
particularly user-friendly and are employed only by a minority of
quantum chemists. Our goal is to popularize these methods by making
it easier to make good active space choices. We present a machine
learning protocol that performs an automated selection of active spaces
for chemical bond dissociation calculations of main group diatomic
molecules. The protocol shows high prediction performance for a given
target system as long as a properly correlated system is chosen for
training. Good active spaces are correctly predicted with a considerably
better success rate than random guess (larger than 80% precision for
most systems studied). Our automated machine learning protocol shows
that a “black-box” mode is possible for facilitating
and accelerating the large-scale calculations on multireference systems
where single-reference methods such as KS-DFT cannot be applied.
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KS-DFTchemical bondMultireference Methodssuccess rateCASSCFquantum chemistryconfiguration interaction calculationspace methodschemical bond dissociation calculationsChemical Bond Dissociationspace choicesActive Space Selectionsingle-reference methodsmultireference systemsMulticonfigurational wave functionschemical bondsquantum chemiststarget systemprediction performanceMachine Learningdensity functionals
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