Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design

Janet, Jon Paul; Gani, Terry Z. H.; Steeves, Adam H.; Ioannidis, Efthymios I.; Kulik, Heather J.

doi:10.1021/acs.iecr.7b00808

Cited by 56Machine Learning in Materials ScienceAdvanced Chemical Physics StudiesComputational Drug Discovery Methods

Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design

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Janet, Jon Paul, Gani, Terry Z. H., Steeves, Adam H., Ioannidis, Efthymios I., Kulik, Heather J.·2017·Industrial & Engineering Chemistry Research

DOI 10.1021/acs.iecr.7b00808

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Abstract

Virtual high throughput screening, typically driven by first-principles, density functional theory calculations, has emerged as a powerful tool for the discovery of new materials. Although the computational materials science community has benefited from open source tools for the rapid structure generation, calculation, and analysis of crystalline inorganic materials, software and strategies to address the unique challenges of inorganic complex discovery have not been as widely available. We present a unified view of our recent developments in the open source molSimplify code for inorganic discovery. Building on our previous efforts in the automated generation of highly accurate inorganic molecular structures, first-principles simulation, and property analysis to accelerate high-throughput screening, we have recently incorporated a neural network that both improves structure generation and predicts electronic properties prior to first-principles calculation. We also provide an overview of how multimillion molecule organic libraries can be leveraged for inorganic discovery alongside cheminformatics concepts of molecular diversity in order to efficiently traverse chemical space. We demonstrate all of these tools on the discovery of design rules for octahedral Fe(II/III) redox couples with nitrogen ligands. Over a search of only approximately 40 new molecules, we obtain redox potentials relative to the Fc/Fc + couple ranging from −1 to 4.5 V in aqueous solution. Our new automated correlation analysis reveals heteroatom identity and the degree of structural branching to be key ligand descriptors in determining redox potential. This inorganic discovery toolkit provides a promising approach to advancing transition metal complex design.

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Beyond trial-and-error or ad hoc selection of descriptors, rigorous feature selection with ML models has emerged as a powerful tool for inferring design principles.
“Beyond trial-and-error or ad hoc selection of descriptors, rigorous feature selection with ML models has emerged as a powerful tool for inferring design principles (22,32,43,52,72,90,91).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [90]

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Authors: Janet, J. P., Gani, T. Z. H., Steeves, A. H., Ioannidis, E. I., & Kulik, H. J.
Affiliations: Massachusetts Institute of Technology (US)
Corresponding author: Kulik, Heather J.
ORCID: Janet, Jon Paul
Gani, Terry Z. H.
Steeves, Adam H.
Ioannidis, Efthymios I.
Kulik, Heather J.
Venue: Industrial & Engineering Chemistry Research
Publisher: American Chemical Society (ACS)
Published: 2017-04-19
Volume: 56
Issue: 17
Pages: 4898-4910
ISSN: 0888-5885 (ISSN-L)
1520-5045
DOI: 10.1021/acs.iecr.7b00808
OpenAlex: W2604906708
Cited by: 56
References: 113
Citations by year: 2026: 3 · 2025: 3 · 2024: 3 · 2023: 5 · 2022: 5 · 2021: 8 · 2020: 10 · 2019: 10 · 2018: 6 · 2017: 3
FWCI: 3.35
Type: journal article
Language: en
Open access: green
hdl.handle.net
Licence: cc-by-nc
Locations: Industrial & Engineering Chemistry Research (publishedVersion)
DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
Funders: Burroughs Wellcome Fund (Career Award at the Scientific Interface)
National Science Foundation (ECCS-1449291)
Topics: Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords: high-throughput virtual screening, redox potential prediction, N-donor ligands, molecular diversity, chemical space exploration, ligand descriptors, open source software

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Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design

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