Many-body expansion based machine learning models for octahedral transition metal complexes
Name
Meyer_2024_Mach._Learn.__Sci._Technol._5_045080.pdf
Description
Published version
Size
1.69 MB
Format
Adobe PDF
Checksum (MD5)
70f315fe237af513363a937601edbf4d
Author(s)
Meyer, Ralf
Chu, Daniel BK
Kulik, Heather J
Date Issued
January 6, 2025
Journal
Machine Learning: Science and Technology
Publisher
IOP Publishing
Citation
Ralf Meyer et al 2024 Mach. Learn.: Sci. Technol. 5 045080
Version
Final published version
Abstract
Graph-based machine learning (ML) models for material properties show great potential to accelerate virtual high-throughput screening of large chemical spaces. However, in their simplest forms, graph-based models do not include any 3D information and are unable to distinguish stereoisomers such as those arising from different orderings of ligands around a metal center in coordination complexes. In this work we present a modification to revised autocorrelation descriptors, a molecular graph featurization method, for predicting spin state dependent properties of octahedral transition metal complexes (TMCs). Inspired by analytical semi-empirical models for TMCs, the new modeling strategy is based on the many-body expansion (MBE) and allows one to tune the captured stereoisomer information by changing the truncation order of the MBE. We present the necessary modifications to include this approach in two commonly used ML methods, kernel ridge regression and feed-forward neural networks. On a test set composed of all possible isomers of binary TMCs, the best MBE models achieve mean absolute errors (MAEs) of 2.75 kcal mol−1 on spin-splitting energies and 0.26 eV on frontier orbital energy gaps, a 30%–40% reduction in error compared to models based on our previous approach. We also observe improved generalization to previously unseen ligands where the best-performing models exhibit MAEs of 4.00 kcal mol−1 (i.e. a 0.73 kcal mol−1 reduction) on the spin-splitting energies and 0.53 eV (i.e. a 0.10 eV reduction) on the frontier orbital energy gaps. Because the new approach incorporates insights from electronic structure theory, such as ligand additivity relationships, these models exhibit systematic generalization from homoleptic to heteroleptic complexes, allowing for efficient screening of TMC search spaces.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Chemistry
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1088/2632-2153/ad9f22
