Making machine learning a useful tool in the accelerated discovery of transition metal complexes

Kulik, Heather J.

doi:10.1002/wcms.1439

Title: Making machine learning a useful tool in the accelerated discovery of transition metal complexes

Journal Article · Fri Aug 02 00:00:00 EDT 2019 · Wiley Interdisciplinary Reviews: Computational Molecular Science

DOI:https://doi.org/10.1002/wcms.1439· OSTI ID:1546100

^[1]

Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts

Abstract As machine learning (ML) has matured, it has opened a new frontier in theoretical and computational chemistry by offering the promise of simultaneous paradigm shifts in accuracy and efficiency. Nowhere is this advance more needed, but also more challenging to achieve, than in the discovery of open‐shell transition metal complexes. Here, localized d or f electrons exhibit variable bonding that is challenging to capture even with the most computationally demanding methods. Thus, despite great promise, clear obstacles remain in constructing ML models that can supplement or even replace explicit electronic structure calculations. In this article, I outline the recent advances in building ML models in transition metal chemistry, including the ability to approach sub‐kcal/mol accuracy on a range of properties with tailored representations, to discover and enumerate complexes in large chemical spaces, and to reveal opportunities for design through analysis of feature importance. I discuss unique considerations that have been essential to enabling ML in open‐shell transition metal chemistry, including (a) the relationship of data set size/diversity, model complexity, and representation choice, (b) the importance of quantitative assessments of both theory and model domain of applicability, and (c) the need to enable autonomous generation of reliable, large data sets both for ML model training and in active learning or discovery contexts. Finally, I summarize the next steps toward making ML a mainstream tool in the accelerated discovery of transition metal complexes. This article is categorized under: Electronic Structure Theory > Density Functional Theory Software > Molecular Modeling Computer and Information Science > Chemoinformatics

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Sponsoring Organization:: USDOE

OSTI ID:: 1546100

Journal Information:: Wiley Interdisciplinary Reviews: Computational Molecular Science, Journal Name: Wiley Interdisciplinary Reviews: Computational Molecular Science Vol. 10 Journal Issue: 1; ISSN 1759-0876

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: United States

Language:: English

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