Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery
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Chu_JCIM_2024.pdf
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Accepted version
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Author(s)
Chu, Daniel BK
González-Narváez, David A
Meyer, Ralf
Nandy, Aditya
Kulik, Heather J
Date Issued
November 28, 2024
Journal
Journal of Chemical Information and Modeling
Publisher
American Chemical Society
Citation
Chu, Daniel BK, González-Narváez, David A, Meyer, Ralf, Nandy, Aditya and Kulik, Heather J. 2024. "Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery." Journal of Chemical Information and Modeling, 64 (24).
Version
Author's final manuscript
Abstract
Methods that accelerate the evaluation of molecular properties are essential for
chemical discovery. While some degree of ligand additivity has been established for transition
metal complexes, it is underutilized in asymmetric complexes, such as the square pyramidal
coordination geometries highly relevant to catalysis. To develop predictive methods beyond
simple additivity, we apply a many-body expansion to octahedral and square pyramidal complexes
and introduce a correction based on adjacent ligands (i.e., the cis interaction model). We first test
the cis interaction model on adiabatic spin-splitting energies of octahedral Fe(II) complexes,
predicting DFT-calculated values of unseen binary complexes to within an average of 1.4 kcal/mol.
Uncertainty analysis reveals the optimal basis, comprising the homoleptic and mer symmetric
complexes. We next show that the cis model (i.e., the cis interaction model solved for the optimal
basis) infers both DFT- and CCSD(T)-calculated model catalytic reaction energies to within 1
kcal/mol on average. The cis model predicts low-symmetry complexes with reaction energies
outside the range of binary complex reaction energies. We observe that trans interactions are
unnecessary for most monodentate systems but can be important for some combinations of ligands,
such as complexes containing a mixture of bidentate and monodentate ligands. Finally, we
demonstrate that the cis model may be combined with D-learning to predict CCSD(T) reaction
energies from exhaustively calculated DFT reaction energies and the same fraction of CCSD(T)
reaction energies needed for the cis model, achieving around 30% of the error from using the
CCSD(T) reaction energies in the cis model alone.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Chemistry
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DOI of Published Version
10.1021/acs.jcim.4c01728
