Deciphering Cryptic Behavior in Bimetallic Transition-Metal Complexes with Machine Learning

Michael G. Taylor; Aditya Nandy; Connie C. Lu; Heather J. Kulik

doi:10.1021/acs.jpclett.1c02852

Deciphering Cryptic Behavior in Bimetallic Transition-Metal Complexes with Machine Learning

Michael G. Taylor, Aditya Nandy, Connie C. Lu, Heather J. Kulik

Chemistry (Twin Cities)

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

We demonstrate an alternative, data-driven approach to uncovering structure-property relationships for the rational design of heterobimetallic transition-metal complexes that exhibit metal-metal bonding. We tailor graph-based representations of the metal-local environment for these complexes for use in multiple linear regression and kernel ridge regression (KRR) models. We curate a set of 28 experimentally characterized complexes to develop a multiple linear regression model for oxidation potentials. We achieve good accuracy (mean absolute error of 0.25 V) and preserve transferability to unseen experimental data with a new ligand structure. We also train a KRR model on a subset of 330 structurally characterized heterobimetallics to predict the degree of metal-metal bonding. This KRR model predicts relative metal-metal bond lengths in the test set to within 5%, and analysis of key features reveals the fundamental atomic contributions (e.g., the valence electron configuration) that most strongly influence the behavior of these complexes. Our work provides guidance for rational bimetallic design, suggesting that properties, including the formal shortness ratio, should be transferable from one period to another.

Original language	English (US)
Pages (from-to)	9812-9820
Number of pages	9
Journal	Journal of Physical Chemistry Letters
Volume	12
Issue number	40
DOIs	https://doi.org/10.1021/acs.jpclett.1c02852
State	Published - Oct 14 2021

Bibliographical note

Funding Information:
This work is supported as part of the Inorganometallic Catalysis Design Center, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, via Grant DE-SC0012702. A.N. was partially supported by a National Science Foundation Graduate Research Fellowship under Grant 1122374. Database construction and infrastructure were partially supported by the Office of Naval Research under Grant N00014-20-1-2150. H.J.K. holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund and an AAAS Marion Milligan Mason Award, which supported this work. The authors thank Adam H. Steeves for providing a critical reading of the manuscript.

Publisher Copyright:
© 2021 American Chemical Society.

PubMed: MeSH publication types

Journal Article

Publisher link

10.1021/acs.jpclett.1c02852

Find It

Find It at U of M Libraries

Cite this

@article{da0fc3affc744ab6b1e0363474135fe3,

title = "Deciphering Cryptic Behavior in Bimetallic Transition-Metal Complexes with Machine Learning",

abstract = "We demonstrate an alternative, data-driven approach to uncovering structure-property relationships for the rational design of heterobimetallic transition-metal complexes that exhibit metal-metal bonding. We tailor graph-based representations of the metal-local environment for these complexes for use in multiple linear regression and kernel ridge regression (KRR) models. We curate a set of 28 experimentally characterized complexes to develop a multiple linear regression model for oxidation potentials. We achieve good accuracy (mean absolute error of 0.25 V) and preserve transferability to unseen experimental data with a new ligand structure. We also train a KRR model on a subset of 330 structurally characterized heterobimetallics to predict the degree of metal-metal bonding. This KRR model predicts relative metal-metal bond lengths in the test set to within 5%, and analysis of key features reveals the fundamental atomic contributions (e.g., the valence electron configuration) that most strongly influence the behavior of these complexes. Our work provides guidance for rational bimetallic design, suggesting that properties, including the formal shortness ratio, should be transferable from one period to another.",

author = "Taylor, {Michael G.} and Aditya Nandy and Lu, {Connie C.} and Kulik, {Heather J.}",

note = "Funding Information: This work is supported as part of the Inorganometallic Catalysis Design Center, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, via Grant DE-SC0012702. A.N. was partially supported by a National Science Foundation Graduate Research Fellowship under Grant 1122374. Database construction and infrastructure were partially supported by the Office of Naval Research under Grant N00014-20-1-2150. H.J.K. holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund and an AAAS Marion Milligan Mason Award, which supported this work. The authors thank Adam H. Steeves for providing a critical reading of the manuscript. Publisher Copyright: {\textcopyright} 2021 American Chemical Society.",

year = "2021",

month = oct,

day = "14",

doi = "10.1021/acs.jpclett.1c02852",

language = "English (US)",

volume = "12",

pages = "9812--9820",

journal = "Journal of Physical Chemistry Letters",

issn = "1948-7185",

publisher = "American Chemical Society",

number = "40",

}

TY - JOUR

T1 - Deciphering Cryptic Behavior in Bimetallic Transition-Metal Complexes with Machine Learning

AU - Taylor, Michael G.

AU - Nandy, Aditya

AU - Lu, Connie C.

AU - Kulik, Heather J.

N1 - Funding Information: This work is supported as part of the Inorganometallic Catalysis Design Center, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, via Grant DE-SC0012702. A.N. was partially supported by a National Science Foundation Graduate Research Fellowship under Grant 1122374. Database construction and infrastructure were partially supported by the Office of Naval Research under Grant N00014-20-1-2150. H.J.K. holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund and an AAAS Marion Milligan Mason Award, which supported this work. The authors thank Adam H. Steeves for providing a critical reading of the manuscript. Publisher Copyright: © 2021 American Chemical Society.

PY - 2021/10/14

Y1 - 2021/10/14

N2 - We demonstrate an alternative, data-driven approach to uncovering structure-property relationships for the rational design of heterobimetallic transition-metal complexes that exhibit metal-metal bonding. We tailor graph-based representations of the metal-local environment for these complexes for use in multiple linear regression and kernel ridge regression (KRR) models. We curate a set of 28 experimentally characterized complexes to develop a multiple linear regression model for oxidation potentials. We achieve good accuracy (mean absolute error of 0.25 V) and preserve transferability to unseen experimental data with a new ligand structure. We also train a KRR model on a subset of 330 structurally characterized heterobimetallics to predict the degree of metal-metal bonding. This KRR model predicts relative metal-metal bond lengths in the test set to within 5%, and analysis of key features reveals the fundamental atomic contributions (e.g., the valence electron configuration) that most strongly influence the behavior of these complexes. Our work provides guidance for rational bimetallic design, suggesting that properties, including the formal shortness ratio, should be transferable from one period to another.

AB - We demonstrate an alternative, data-driven approach to uncovering structure-property relationships for the rational design of heterobimetallic transition-metal complexes that exhibit metal-metal bonding. We tailor graph-based representations of the metal-local environment for these complexes for use in multiple linear regression and kernel ridge regression (KRR) models. We curate a set of 28 experimentally characterized complexes to develop a multiple linear regression model for oxidation potentials. We achieve good accuracy (mean absolute error of 0.25 V) and preserve transferability to unseen experimental data with a new ligand structure. We also train a KRR model on a subset of 330 structurally characterized heterobimetallics to predict the degree of metal-metal bonding. This KRR model predicts relative metal-metal bond lengths in the test set to within 5%, and analysis of key features reveals the fundamental atomic contributions (e.g., the valence electron configuration) that most strongly influence the behavior of these complexes. Our work provides guidance for rational bimetallic design, suggesting that properties, including the formal shortness ratio, should be transferable from one period to another.

UR - http://www.scopus.com/inward/record.url?scp=85117174673&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85117174673&partnerID=8YFLogxK

U2 - 10.1021/acs.jpclett.1c02852

DO - 10.1021/acs.jpclett.1c02852

M3 - Article

C2 - 34597514

AN - SCOPUS:85117174673

SN - 1948-7185

VL - 12

SP - 9812

EP - 9820

JO - Journal of Physical Chemistry Letters

JF - Journal of Physical Chemistry Letters

IS - 40

ER -

Deciphering Cryptic Behavior in Bimetallic Transition-Metal Complexes with Machine Learning

Abstract

Bibliographical note

PubMed: MeSH publication types

Publisher link

Find It

Other files and links

Fingerprint

Cite this