A Critical Review of Machine Learning of Energy Materials

Chen, Chi; Zuo, Yunxing; Ye, Weike; Li, Xiangguo; Deng, Zhi; Ong, Shyue Ping

doi:10.1002/aenm.201903242

Title: A Critical Review of Machine Learning of Energy Materials

Abstract

Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

Authors:

^[1]; Zuo, Yunxing ^[1]; Ye, Weike ^[1]; Li, Xiangguo ^[1]; Deng, Zhi ^[1];

^[1]

Department of NanoEngineering University of California San Diego 9500 Gilman Dr, Mail Code 0448 La Jolla CA 92093‐0448 USA

Publication Date:: Wed Jan 29 00:00:00 EST 2020

Sponsoring Org.:: USDOE

OSTI Identifier:: 1595872

Resource Type:: Publisher's Accepted Manuscript

Journal Name:: Advanced Energy Materials

Additional Journal Information:: Journal Name: Advanced Energy Materials Journal Volume: 10 Journal Issue: 8; Journal ID: ISSN 1614-6832

Publisher:: Wiley Blackwell (John Wiley & Sons)

Country of Publication:: Germany

Language:: English

Citation Formats


                    Chen, Chi, Zuo, Yunxing, Ye, Weike, Li, Xiangguo, Deng, Zhi, and Ong, Shyue Ping. A Critical Review of Machine Learning of Energy Materials.  Germany: N. p., 2020. 
Web.  doi:10.1002/aenm.201903242.

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                    Chen, Chi, Zuo, Yunxing, Ye, Weike, Li, Xiangguo, Deng, Zhi, & Ong, Shyue Ping. A Critical Review of Machine Learning of Energy Materials.  Germany.  https://doi.org/10.1002/aenm.201903242

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                    Chen, Chi, Zuo, Yunxing, Ye, Weike, Li, Xiangguo, Deng, Zhi, and Ong, Shyue Ping. Wed .  
"A Critical Review of Machine Learning of Energy Materials".  Germany.  https://doi.org/10.1002/aenm.201903242.

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@article{osti_1595872,

  title        = {A Critical Review of Machine Learning of Energy Materials},

  author       = {Chen, Chi and Zuo, Yunxing and Ye, Weike and Li, Xiangguo and Deng, Zhi and Ong, Shyue Ping},

  abstractNote = {Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.},

  doi          = {10.1002/aenm.201903242},

  journal      = {Advanced Energy Materials},

  number       = 8,

  volume       = 10,

  place        = {Germany},

  year         = {Wed Jan 29 00:00:00 EST 2020},

  month        = {Wed Jan 29 00:00:00 EST 2020}

}

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Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods journal, April 2017

Machine learning of molecular electronic properties in chemical compound space journal, September 2013

Understanding band gaps of solids in generalized Kohn–Sham theory journal, March 2017

Extrapolating Energetics on Clusters and Single-Crystal Surfaces to Nanoparticles by Machine-Learning Scheme journal, November 2017

Virtual Exploration of the Chemical Universe up to 11 Atoms of C, N, O, F: Assembly of 26.4 Million Structures (110.9 Million Stereoisomers) and Analysis for New Ring Systems, Stereochemistry, Physicochemical Properties, Compound Classes, and Drug Discovery journal, January 2007

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials journal, January 2016

Transferable Machine-Learning Model of the Electron Density journal, December 2018

Density functional theory in surface chemistry and catalysis journal, January 2011

The Inorganic Crystal Structure Database (ICSD)—Present and Future journal, January 2004

Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis journal, September 2019

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Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors journal, July 2018

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures journal, June 2018

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules journal, January 2018

Application of a neural network to the analysis of catalytic reactions Analysis of NO decomposition over Cu/ZSM-5 zeolite journal, November 1995

Ultimate Limits to Intercalation Reactions for Lithium Batteries journal, October 2014

Extending the accuracy of the SNAP interatomic potential form journal, June 2018

Identifying Active Sites for CO 2 Reduction on Dealloyed Gold Surfaces by Combining Machine Learning with Multiscale Simulations journal, June 2019

A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction journal, April 2019

Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling journal, November 2003

Machine learning in electronic-quantum-matter imaging experiments journal, June 2019

New tolerance factor to predict the stability of perovskite oxides and halides journal, February 2019

Accelerating the discovery of materials for clean energy in the era of smart automation journal, April 2018

Explicit representation of knowledge acquired from plant historical data using neural network conference, January 1990

Neural network predictions of oxygen interactions on a dynamic Pd surface journal, January 2017

Exploring materials band structure space with unsupervised machine learning journal, February 2019

Deep elastic strain engineering of bandgap through machine learning journal, February 2019

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach journal, April 2015

The Linus Pauling file (LPF) and its application to materials design journal, September 1998

The GW method journal, March 1998

Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces journal, April 2007

Recent advances and applications of machine learning in solid-state materials science journal, August 2019

Superconductivity at 250 K in lanthanum hydride under high pressures journal, May 2019

High-throughput computational X-ray absorption spectroscopy journal, July 2018

Big Data of Materials Science: Critical Role of the Descriptor journal, March 2015

A high-throughput infrastructure for density functional theory calculations journal, June 2011

The Harvard organic photovoltaic dataset journal, September 2016

Chemical shifts in molecular solids by machine learning journal, October 2018

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons journal, April 2010

The Protein Data Bank journal, January 2000

Occurrence of the potent mutagens 2- nitrobenzanthrone and 3-nitrobenzanthrone in fine airborne particles journal, January 2019

Neural network as a tool for catalyst development journal, April 1995

Active learning of linearly parametrized interatomic potentials journal, December 2017

Predictive analytics for crystalline materials: bulk modulus journal, January 2016

Chemical Pressure-Driven Enhancement of the Hydrogen Evolving Activity of Ni 2 P from Nonmetal Surface Doping Interpreted via Machine Learning journal, March 2018

Symbolic regression in materials science journal, June 2019

Data-driven approach for the prediction and interpretation of core-electron loss spectroscopy journal, September 2018

From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown journal, February 2016

Accurate force field for molybdenum by machine learning large materials data journal, September 2017

Machine learning meets volcano plots: computational discovery of cross-coupling catalysts journal, January 2018

Unraveling the effect of La A-site substitution on oxygen ion diffusion and oxygen catalysis in perovskite BaFeO 3 by data-mining molecular dynamics and density functional theory journal, January 2015

Multi-objective Optimization for Materials Discovery via Adaptive Design journal, February 2018

An open experimental database for exploring inorganic materials journal, April 2018

Extended-Connectivity Fingerprints journal, April 2010

Deep learning for computational chemistry journal, March 2017

The emergence of perovskite solar cells journal, July 2014

Charting the complete elastic properties of inorganic crystalline compounds journal, March 2015

“Inverting” X-ray Absorption Spectra of Catalysts by Machine Learning in Search for Activity Descriptors
journal, September 2019

Double-slit photoelectron interference in strong-field ionization of the neon dimer
journal, January 2019

CALYPSO: A method for crystal structure prediction
journal, October 2012

Deep learning
journal, May 2015

BoltzTraP2, a program for interpolating band structures and calculating semi-classical transport coefficients
journal, October 2018

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships
journal, April 2016

The Principles Determining the Structure of Complex Ionic Crystals
journal, April 1929

Accelerated Discovery of Large Electrostrains in BaTiO ₃ -Based Piezoelectrics Using Active Learning
journal, January 2018

Amp: A modular approach to machine learning in atomistic simulations
journal, October 2016

Springer Handbook of Electronic and Photonic Materials
book, January 2017

Organometal Halide Perovskites as Visible-Light Sensitizers for Photovoltaic Cells
journal, May 2009

Machine learning for heterogeneous catalyst design and discovery
journal, May 2018

The metallization and superconductivity of dense hydrogen sulfide
journal, May 2014

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
journal, June 2015

Structure and structural evolution of () clusters using a genetic algorithm and density functional theory method
journal, October 2007

Machine learning in catalysis
journal, April 2018

Data-driven exploration of new pressure-induced superconductivity in PbBi 2 Te 4
journal, August 2018

The geometric blueprint of perovskites
journal, May 2018

Deep neural networks for accurate predictions of crystal stability
journal, September 2018

Formability of ABO3 perovskites
journal, June 2004

Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods
journal, April 2017

Machine learning of molecular electronic properties in chemical compound space
journal, September 2013

Understanding band gaps of solids in generalized Kohn–Sham theory
journal, March 2017

Extrapolating Energetics on Clusters and Single-Crystal Surfaces to Nanoparticles by Machine-Learning Scheme
journal, November 2017

Virtual Exploration of the Chemical Universe up to 11 Atoms of C, N, O, F: Assembly of 26.4 Million Structures (110.9 Million Stereoisomers) and Analysis for New Ring Systems, Stereochemistry, Physicochemical Properties, Compound Classes, and Drug Discovery
journal, January 2007

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
journal, January 2016

Transferable Machine-Learning Model of the Electron Density
journal, December 2018

Density functional theory in surface chemistry and catalysis
journal, January 2011

The Inorganic Crystal Structure Database (ICSD)—Present and Future
journal, January 2004

Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis
journal, September 2019

Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning
journal, December 2016

Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors
journal, July 2018

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures
journal, June 2018

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
journal, January 2018

Application of a neural network to the analysis of catalytic reactions Analysis of NO decomposition over Cu/ZSM-5 zeolite
journal, November 1995

Ultimate Limits to Intercalation Reactions for Lithium Batteries
journal, October 2014

Extending the accuracy of the SNAP interatomic potential form
journal, June 2018

Identifying Active Sites for CO 2 Reduction on Dealloyed Gold Surfaces by Combining Machine Learning with Multiscale Simulations
journal, June 2019

A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction
journal, April 2019

Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
journal, November 2003

Machine learning in electronic-quantum-matter imaging experiments
journal, June 2019

New tolerance factor to predict the stability of perovskite oxides and halides
journal, February 2019

Accelerating the discovery of materials for clean energy in the era of smart automation
journal, April 2018

Explicit representation of knowledge acquired from plant historical data using neural network
conference, January 1990

Neural network predictions of oxygen interactions on a dynamic Pd surface
journal, January 2017

Exploring materials band structure space with unsupervised machine learning
journal, February 2019

Deep elastic strain engineering of bandgap through machine learning
journal, February 2019

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
journal, April 2015

The Linus Pauling file (LPF) and its application to materials design
journal, September 1998

The GW method
journal, March 1998

Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
journal, April 2007

Recent advances and applications of machine learning in solid-state materials science
journal, August 2019

Superconductivity at 250 K in lanthanum hydride under high pressures
journal, May 2019

High-throughput computational X-ray absorption spectroscopy
journal, July 2018

Big Data of Materials Science: Critical Role of the Descriptor
journal, March 2015

A high-throughput infrastructure for density functional theory calculations
journal, June 2011

The Harvard organic photovoltaic dataset
journal, September 2016

Chemical shifts in molecular solids by machine learning
journal, October 2018

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
journal, April 2010

The Protein Data Bank
journal, January 2000

Occurrence of the potent mutagens 2- nitrobenzanthrone and 3-nitrobenzanthrone in fine airborne particles
journal, January 2019

Neural network as a tool for catalyst development
journal, April 1995

Active learning of linearly parametrized interatomic potentials
journal, December 2017

Predictive analytics for crystalline materials: bulk modulus
journal, January 2016

Chemical Pressure-Driven Enhancement of the Hydrogen Evolving Activity of Ni ₂ P from Nonmetal Surface Doping Interpreted via Machine Learning
journal, March 2018

Symbolic regression in materials science
journal, June 2019

Data-driven approach for the prediction and interpretation of core-electron loss spectroscopy
journal, September 2018

From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown
journal, February 2016

Accurate force field for molybdenum by machine learning large materials data
journal, September 2017

Machine learning meets volcano plots: computational discovery of cross-coupling catalysts
journal, January 2018

Unraveling the effect of La A-site substitution on oxygen ion diffusion and oxygen catalysis in perovskite BaFeO 3 by data-mining molecular dynamics and density functional theory
journal, January 2015

Multi-objective Optimization for Materials Discovery via Adaptive Design
journal, February 2018

An open experimental database for exploring inorganic materials
journal, April 2018

Extended-Connectivity Fingerprints
journal, April 2010

Deep learning for computational chemistry
journal, March 2017

The emergence of perovskite solar cells
journal, July 2014

Charting the complete elastic properties of inorganic crystalline compounds
journal, March 2015

Active learning with non- ab initio input features toward efficient CO ₂ reduction catalysts
journal, January 2018

PZT phase diagram determination by measurement of elastic moduli
journal, August 2005

FireWorks: a dynamic workflow system designed for high-throughput applications: FireWorks: A Dynamic Workflow System Designed for High-Throughput Applications
journal, May 2015