Issue 1, 2020

Enumeration of de novo inorganic complexes for chemical discovery and machine learning

Abstract

Despite being attractive targets for functional materials, the discovery of transition metal complexes with high-throughput computational screening is challenged by the amount of feasible coordination numbers, spin states, or oxidation states and the potentially large sizes of ligands. To overcome these limitations, we take inspiration from organic chemistry where full enumeration of neutral, closed-shell molecules under the constraint of size has enriched discovery efforts. We design monodentate and bidentate ligands from scratch for the construction of mononuclear, octahedral transition metal complexes with up to 13 heavy atoms (i.e., metal, C, N, O, P, or S). From >11 000 theoretical ligands, we develop a heuristic score for ranking a chemically feasible 2500 ligand subset, only 71 of which were previously included in common organic molecule databases. We characterize the top 20% of scored ligands with density functional theory (DFT) in an octahedral homoleptic ligand database (OHLDB). The OHLDB contains i) the geometry optimized structures of 1250 homoleptic octahedral complexes obtained from the enumerated pool of ligands and an open-shell transition metal (M(II)/M(III), M = Cr, Mn, Fe, or Co) and ii) the resulting high-spin/low-spin adiabatic electronic energy differences (ΔEH–L) obtained with hybrid DFT. Over the OHLDB, we observe structure–property (i.e., ΔEH–L) relationships different from those expected on the basis of ligand field arguments or from our prior data sets. Finally, we demonstrate how incorporating OHLDB data into artificial neural network (ANN) training improves ANN out-of-sample performance on much larger transition metal complexes.

Graphical abstract: Enumeration of de novo inorganic complexes for chemical discovery and machine learning

Supplementary files

Article information

Article type
Paper
Submitted
14 jun 2019
Accepted
03 jul 2019
First published
04 jul 2019
This article is Open Access
Creative Commons BY-NC license

Mol. Syst. Des. Eng., 2020,5, 139-152

Enumeration of de novo inorganic complexes for chemical discovery and machine learning

S. Gugler, J. P. Janet and H. J. Kulik, Mol. Syst. Des. Eng., 2020, 5, 139 DOI: 10.1039/C9ME00069K

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements