Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry

Janet, Jon Paul; Liu, Fang; Nandy, Aditya; Duan, Chenru; Yang, Tzuhsiung; Lin, Sean; Kulik, Heather J.

doi:10.1021/acs.inorgchem.9b00109

Title: Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry

Abstract

Recent transformative advances in computing power and algorithms have made computational chemistry central to the discovery and design of new molecules and materials. First-principles simulations are increasingly accurate and applicable to large systems with the speed needed for high-throughput computational screening. Despite these strides, the combinatorial challenges associated with the vastness of chemical space mean that more than just fast and accurate computational tools are needed for accelerated chemical discovery. In transition-metal chemistry and catalysis, unique challenges arise. The variable spin, oxidation state, and coordination environments favored by elements with well-localized d or f electrons provide great opportunity for tailoring properties in catalytic or functional (e.g., magnetic) materials but also add layers of uncertainty to any design strategy. We outline five key mandates for realizing computationally driven accelerated discovery in inorganic chemistry: (i) fully automated simulation of new compounds, (ii) knowledge of prediction sensitivity or accuracy, (iii) faster-than-fast property prediction methods, (iv) maps for rapid chemical space traversal, and (v) a means to reveal design rules on the kilocompound scale. Through case studies in open-shell transition-metal chemistry, we describe how advances in methodology and software in each of these areas bring about new chemical insights. We conclude with our outlookmore »« less

Authors:

^[1];

^[2];

^[1]; Lin, Sean ^[1];

^[1]

Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States, Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

Publication Date:: Tue Mar 05 00:00:00 EST 2019

Research Org.:: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

OSTI Identifier:: 1498804

Alternate Identifier(s):: OSTI ID: 1557696

Grant/Contract Number:: SC0018096; N00014-17-1-2956; N00014-18-1-2434; D18AP00039; CBET-1704266; ACI-1429830

Resource Type:: Published Article

Journal Name:: Inorganic Chemistry

Additional Journal Information:: Journal Name: Inorganic Chemistry Journal Volume: 58 Journal Issue: 16; Journal ID: ISSN 0020-1669

Publisher:: American Chemical Society

Country of Publication:: United States

Language:: English

Subject:: 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats


                    Janet, Jon Paul, Liu, Fang, Nandy, Aditya, Duan, Chenru, Yang, Tzuhsiung, Lin, Sean, and Kulik, Heather J. Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry.  United States: N. p., 2019. 
Web.  doi:10.1021/acs.inorgchem.9b00109.

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                    Janet, Jon Paul, Liu, Fang, Nandy, Aditya, Duan, Chenru, Yang, Tzuhsiung, Lin, Sean, & Kulik, Heather J. Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry.  United States.  https://doi.org/10.1021/acs.inorgchem.9b00109

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                    Janet, Jon Paul, Liu, Fang, Nandy, Aditya, Duan, Chenru, Yang, Tzuhsiung, Lin, Sean, and Kulik, Heather J. Tue .  
"Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry".  United States.  https://doi.org/10.1021/acs.inorgchem.9b00109.

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@article{osti_1498804,

  title        = {Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry},

  author       = {Janet, Jon Paul and Liu, Fang and Nandy, Aditya and Duan, Chenru and Yang, Tzuhsiung and Lin, Sean and Kulik, Heather J.},

  abstractNote = {Recent transformative advances in computing power and algorithms have made computational chemistry central to the discovery and design of new molecules and materials. First-principles simulations are increasingly accurate and applicable to large systems with the speed needed for high-throughput computational screening. Despite these strides, the combinatorial challenges associated with the vastness of chemical space mean that more than just fast and accurate computational tools are needed for accelerated chemical discovery. In transition-metal chemistry and catalysis, unique challenges arise. The variable spin, oxidation state, and coordination environments favored by elements with well-localized d or f electrons provide great opportunity for tailoring properties in catalytic or functional (e.g., magnetic) materials but also add layers of uncertainty to any design strategy. We outline five key mandates for realizing computationally driven accelerated discovery in inorganic chemistry: (i) fully automated simulation of new compounds, (ii) knowledge of prediction sensitivity or accuracy, (iii) faster-than-fast property prediction methods, (iv) maps for rapid chemical space traversal, and (v) a means to reveal design rules on the kilocompound scale. Through case studies in open-shell transition-metal chemistry, we describe how advances in methodology and software in each of these areas bring about new chemical insights. We conclude with our outlook on the next steps in this process toward realizing fully autonomous discovery in inorganic chemistry using computational chemistry.},

  doi          = {10.1021/acs.inorgchem.9b00109},

  journal      = {Inorganic Chemistry},

  number       = 16,

  volume       = 58,

  place        = {United States},

  year         = {Tue Mar 05 00:00:00 EST 2019},

  month        = {Tue Mar 05 00:00:00 EST 2019}

}

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Figure 1: Differences between computational high-throughput screening in organic chemistry (top) and inorganic chemistry (bottom). From left to right: structure generation, simulation methodology, database accessibility, and concepts of molecular similarity.

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Works referencing / citing this record:

Enumeration of de novo inorganic complexes for chemical discovery and machine learning
journal, January 2020

Gugler, Stefan; Janet, Jon Paul; Kulik, Heather J.
Molecular Systems Design & Engineering, Vol. 5, Issue 1
DOI: 10.1039/c9me00069k

Enumeration of de novo inorganic complexes for chemical discovery and machine learning
journal, January 2020

Gugler, Stefan; Janet, Jon Paul; Kulik, Heather J.
Molecular Systems Design & Engineering, Vol. 5, Issue 1
DOI: 10.1039/c9me00069k

Making machine learning a useful tool in the accelerated discovery of transition metal complexes
journal, July 2019

Kulik, Heather J.
WIREs Computational Molecular Science, Vol. 10, Issue 1
DOI: 10.1002/wcms.1439

Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences
journal, August 2019

Herr, John E.; Koh, Kevin; Yao, Kun
The Journal of Chemical Physics, Vol. 151, Issue 8
DOI: 10.1063/1.5108803

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Similar Records

Title: Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry

Abstract

Citation Formats

Figures / Tables:

Enumeration of de novo inorganic complexes for chemical discovery and machine learning journal, January 2020

Enumeration of de novo inorganic complexes for chemical discovery and machine learning journal, January 2020

Making machine learning a useful tool in the accelerated discovery of transition metal complexes journal, July 2019

Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences journal, August 2019

Enumeration of de novo inorganic complexes for chemical discovery and machine learning
journal, January 2020

Enumeration of de novo inorganic complexes for chemical discovery and machine learning
journal, January 2020

Making machine learning a useful tool in the accelerated discovery of transition metal complexes
journal, July 2019

Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences
journal, August 2019