skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries

Abstract

Machine-learning (ML) techniques have rapidly found applications in many domains of materials chemistry and physics where large data sets are available. Aiming to accelerate the discovery of materials for battery applications, in this work, we develop a tool (http://se.cmich. edu/batteries) based on ML models to predict voltages of electrode materials for metal-ion batteries. To this end, we use deep neural network, support vector machine, and kernel ridge regression as ML algorithms in combination with data taken from the Materials Project database, as well as feature vectors from properties of chemical compounds and elemental properties of their constituents. We show that our ML models have predictive capabilities for different reference test sets and, as an example, we utilize them to generate a voltage profile diagram and compare it to density functional theory calculations. In addition, using our models, we propose nearly 5000 candidate electrode materials for Na- and K-ion batteries. We also make available a web accessible tool that, within a minute, can be used to estimate the voltage of any bulk electrode material for a number of metal ions. These results show that ML is a promising alternative for computationally demanding calculations as a first screening tool of novel materialsmore » for battery applications.« less

Authors:
 [1];  [2];  [2];  [3];  [3]; ORCiD logo [3]
  1. Central Michigan Univ., Mount Pleasant, MI (United States). Dept. of Physics and Science of Advanced Materials Program and Dept. of Computer Science
  2. Central Michigan Univ., Mount Pleasant, MI (United States). Dept. of Computer Science
  3. Central Michigan Univ., Mount Pleasant, MI (United States). Dept. of Physics and Science of Advanced Materials Program
Publication Date:
Research Org.:
Univ. of Southern California, Los Angeles, CA (United States); Central Michigan Univ., Mount Pleasant, MI (United States); Quantum Information Science (QIS)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division; USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1574907
Alternate Identifier(s):
OSTI ID: 1777848
Grant/Contract Number:  
SC0019432
Resource Type:
Accepted Manuscript
Journal Name:
ACS Applied Materials and Interfaces
Additional Journal Information:
Journal Volume: 11; Journal Issue: 20; Journal ID: ISSN 1944-8244
Publisher:
American Chemical Society (ACS)
Country of Publication:
United States
Language:
English
Subject:
25 ENERGY STORAGE; Quantum Information Science (QIS); machine learning, batteries, intercalation electrodes, web tool, voltage predictor, voltage profile diagram

Citation Formats

Joshi, Rajendra P., Eickholt, Jesse, Li, Liling, Fornari, Marco, Barone, Veronica, and Peralta, Juan E. Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries. United States: N. p., 2019. Web. https://doi.org/10.1021/acsami.9b04933.
Joshi, Rajendra P., Eickholt, Jesse, Li, Liling, Fornari, Marco, Barone, Veronica, & Peralta, Juan E. Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries. United States. https://doi.org/10.1021/acsami.9b04933
Joshi, Rajendra P., Eickholt, Jesse, Li, Liling, Fornari, Marco, Barone, Veronica, and Peralta, Juan E. Mon . "Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries". United States. https://doi.org/10.1021/acsami.9b04933. https://www.osti.gov/servlets/purl/1574907.
@article{osti_1574907,
title = {Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries},
author = {Joshi, Rajendra P. and Eickholt, Jesse and Li, Liling and Fornari, Marco and Barone, Veronica and Peralta, Juan E.},
abstractNote = {Machine-learning (ML) techniques have rapidly found applications in many domains of materials chemistry and physics where large data sets are available. Aiming to accelerate the discovery of materials for battery applications, in this work, we develop a tool (http://se.cmich. edu/batteries) based on ML models to predict voltages of electrode materials for metal-ion batteries. To this end, we use deep neural network, support vector machine, and kernel ridge regression as ML algorithms in combination with data taken from the Materials Project database, as well as feature vectors from properties of chemical compounds and elemental properties of their constituents. We show that our ML models have predictive capabilities for different reference test sets and, as an example, we utilize them to generate a voltage profile diagram and compare it to density functional theory calculations. In addition, using our models, we propose nearly 5000 candidate electrode materials for Na- and K-ion batteries. We also make available a web accessible tool that, within a minute, can be used to estimate the voltage of any bulk electrode material for a number of metal ions. These results show that ML is a promising alternative for computationally demanding calculations as a first screening tool of novel materials for battery applications.},
doi = {10.1021/acsami.9b04933},
journal = {ACS Applied Materials and Interfaces},
number = 20,
volume = 11,
place = {United States},
year = {2019},
month = {4}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 5 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Before Li Ion Batteries
journal, November 2018


Li-ion battery materials: present and future
journal, June 2015


Ion Intercalation into Two-Dimensional Transition-Metal Carbides: Global Screening for New High-Capacity Battery Materials
journal, October 2014

  • Eames, Christopher; Islam, M. Saiful
  • Journal of the American Chemical Society, Vol. 136, Issue 46
  • DOI: 10.1021/ja508154e

Hexagonal BC 3 : A Robust Electrode Material for Li, Na, and K Ion Batteries
journal, June 2015

  • Joshi, Rajendra P.; Ozdemir, Burak; Barone, Veronica
  • The Journal of Physical Chemistry Letters, Vol. 6, Issue 14
  • DOI: 10.1021/acs.jpclett.5b01110

Hexagonal BC 3 Electrode for a High-Voltage Al-Ion Battery
journal, April 2017

  • Bhauriyal, Preeti; Mahata, Arup; Pathak, Biswarup
  • The Journal of Physical Chemistry C, Vol. 121, Issue 18
  • DOI: 10.1021/acs.jpcc.7b02290

Aqueous batteries as grid scale energy storage solutions
journal, February 2017

  • Posada, Jorge Omar Gil; Rennie, Anthony J. R.; Villar, Sofia Perez
  • Renewable and Sustainable Energy Reviews, Vol. 68
  • DOI: 10.1016/j.rser.2016.02.024

Electrical Energy Storage for the Grid: A Battery of Choices
journal, November 2011


Materials for lithium-ion battery safety
journal, June 2018


Is lithium the new gold?
journal, June 2010


A Brief Review on Multivalent Intercalation Batteries with Aqueous Electrolytes
journal, February 2016


Electrical energy storage for transportation—approaching the limits of, and going beyond, lithium-ion batteries
journal, January 2012

  • Thackeray, Michael M.; Wolverton, Christopher; Isaacs, Eric D.
  • Energy & Environmental Science, Vol. 5, Issue 7
  • DOI: 10.1039/c2ee21892e

Chemical space
journal, December 2004

  • Kirkpatrick, Peter; Ellis, Clare
  • Nature, Vol. 432, Issue 7019
  • DOI: 10.1038/432823a

Quantum Machine Learning in Chemical Compound Space
journal, March 2018

  • von Lilienfeld, O. Anatole
  • Angewandte Chemie International Edition, Vol. 57, Issue 16
  • DOI: 10.1002/anie.201709686

The drug-maker's guide to the galaxy
journal, September 2017


Probabilistic machine learning and artificial intelligence
journal, May 2015


Machine learning: Trends, perspectives, and prospects
journal, July 2015


The 2019 materials by design roadmap
journal, October 2018

  • Alberi, Kirstin; Nardelli, Marco Buongiorno; Zakutayev, Andriy
  • Journal of Physics D: Applied Physics, Vol. 52, Issue 1
  • DOI: 10.1088/1361-6463/aad926

AFLOW: An automatic framework for high-throughput materials discovery
journal, June 2012


AFLOW-ML: A RESTful API for machine-learning predictions of materials properties
journal, September 2018


Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
journal, July 2013

  • Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy
  • APL Materials, Vol. 1, Issue 1
  • DOI: 10.1063/1.4812323

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
journal, September 2013


The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
journal, December 2015


NOMAD: The FAIR concept for big data-driven materials science
journal, September 2018


Fermi-Löwdin orbital self-interaction correction to magnetic exchange couplings
journal, October 2018

  • Joshi, Rajendra P.; Trepte, Kai; Withanage, Kushantha P. K.
  • The Journal of Chemical Physics, Vol. 149, Issue 16
  • DOI: 10.1063/1.5050809

Band gap tunning in BN-doped graphene systems with high carrier mobility
journal, February 2014

  • Kaloni, T. P.; Joshi, R. P.; Adhikari, N. P.
  • Applied Physics Letters, Vol. 104, Issue 7
  • DOI: 10.1063/1.4866383

Machine learning in materials informatics: recent applications and prospects
journal, December 2017

  • Ramprasad, Rampi; Batra, Rohit; Pilania, Ghanshyam
  • npj Computational Materials, Vol. 3, Issue 1
  • DOI: 10.1038/s41524-017-0056-5

Active learning for accelerated design of layered materials
journal, December 2018

  • Bassman, Lindsay; Rajak, Pankaj; Kalia, Rajiv K.
  • npj Computational Materials, Vol. 4, Issue 1
  • DOI: 10.1038/s41524-018-0129-0

A strategy to apply machine learning to small datasets in materials science
journal, May 2018


Machine learning for molecular and materials science
journal, July 2018


From DFT to machine learning: recent approaches to materials science–a review
journal, May 2019

  • Schleder, Gabriel R.; Padilha, Antonio C. M.; Acosta, Carlos Mera
  • Journal of Physics: Materials, Vol. 2, Issue 3
  • DOI: 10.1088/2515-7639/ab084b

Representation of compounds for machine-learning prediction of physical properties
journal, April 2017


Combinatorial screening for new materials in unconstrained composition space with machine learning
journal, March 2014


Bypassing the Kohn-Sham equations with machine learning
journal, October 2017


Deep learning and the Schrödinger equation
journal, October 2017


Understanding machine-learned density functionals: Understanding Machine-Learned Density Functionals
journal, November 2015

  • Li, Li; Snyder, John C.; Pelaschier, Isabelle M.
  • International Journal of Quantum Chemistry, Vol. 116, Issue 11
  • DOI: 10.1002/qua.25040

A general-purpose machine learning framework for predicting properties of inorganic materials
journal, August 2016


Machine Learning Energies of 2 Million Elpasolite ( A B C 2 D 6 ) Crystals
journal, September 2016


Predicting density functional theory total energies and enthalpies of formation of metal-nonmetal compounds by linear regression
journal, February 2016


Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials
journal, January 2017

  • Sendek, Austin D.; Yang, Qian; Cubuk, Ekin D.
  • Energy & Environmental Science, Vol. 10, Issue 1
  • DOI: 10.1039/C6EE02697D

Universal fragment descriptors for predicting properties of inorganic crystals
journal, June 2017

  • Isayev, Olexandr; Oses, Corey; Toher, Cormac
  • Nature Communications, Vol. 8, Issue 1
  • DOI: 10.1038/ncomms15679

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


Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry
journal, June 2018

  • Rupp, Matthias; von Lilienfeld, O. Anatole; Burke, Kieron
  • The Journal of Chemical Physics, Vol. 148, Issue 24
  • DOI: 10.1063/1.5043213

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

  • Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan
  • The Journal of Physical Chemistry Letters, Vol. 6, Issue 12
  • DOI: 10.1021/acs.jpclett.5b00831

Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations
journal, July 2017


Deep learning
journal, May 2015

  • LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey
  • Nature, Vol. 521, Issue 7553
  • DOI: 10.1038/nature14539

What is a support vector machine?
journal, December 2006


LIII. On lines and planes of closest fit to systems of points in space
journal, November 1901

  • Pearson, Karl
  • The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, Vol. 2, Issue 11
  • DOI: 10.1080/14786440109462720

An effective method to screen sodium-based layered materials for sodium ion batteries
journal, March 2018


Voltage, stability and diffusion barrier differences between sodium-ion and lithium-ion intercalation materials
journal, January 2011

  • Ong, Shyue Ping; Chevrier, Vincent L.; Hautier, Geoffroy
  • Energy & Environmental Science, Vol. 4, Issue 9
  • DOI: 10.1039/c1ee01782a

β-NaMnO 2 : A High-Performance Cathode for Sodium-Ion Batteries
journal, November 2014

  • Billaud, Juliette; Clément, Raphaële J.; Armstrong, A. Robert
  • Journal of the American Chemical Society, Vol. 136, Issue 49
  • DOI: 10.1021/ja509704t

Sodium intercalation/de-intercalation mechanism in Na4MnV(PO4)3 cathode materials
journal, December 2018


NaFe0.5Co0.5O2 as high energy and power positive electrode for Na-ion batteries
journal, September 2013


Potassium-ion Intercalation Mechanism in Layered Na2Mn3O7
journal, September 2018

  • Sada, Krishnakanth; Senthilkumar, Baskar; Barpanda, Prabeer
  • ACS Applied Energy Materials
  • DOI: 10.1021/acsaem.8b01016

Odyssey of Multivalent Cathode Materials: Open Questions and Future Challenges
journal, February 2017

  • Canepa, Pieremanuele; Sai Gautam, Gopalakrishnan; Hannah, Daniel C.
  • Chemical Reviews, Vol. 117, Issue 5
  • DOI: 10.1021/acs.chemrev.6b00614

Rechargeable magnesium-ion battery based on a TiSe2-cathode with d-p orbital hybridized electronic structure
journal, July 2015

  • Gu, Yunpeng; Katsura, Yukari; Yoshino, Takafumi
  • Scientific Reports, Vol. 5, Issue 1
  • DOI: 10.1038/srep12486

Electrochemical and Spectroscopic Analysis of Mg 2+ Intercalation into Thin Film Electrodes of Layered Oxides: V 2 O 5 and MoO 3
journal, August 2013

  • Gershinsky, Gregory; Yoo, Hyun Deog; Gofer, Yosef
  • Langmuir, Vol. 29, Issue 34
  • DOI: 10.1021/la402391f

Preparation and Characterization of a Stable FeSO 4 F-Based Framework for Alkali Ion Insertion Electrodes
journal, November 2012

  • Recham, Nadir; Rousse, Gwenaëlle; Sougrati, Moulay T.
  • Chemistry of Materials, Vol. 24, Issue 22
  • DOI: 10.1021/cm302428w

TiS2 as a high performance potassium ion battery cathode in ether-based electrolyte
journal, May 2018


    Works referencing / citing this record:

    Ab initio modeling and design of vanadia-based electrode materials for post-lithium batteries
    journal, December 2019


    A Critical Review of Machine Learning of Energy Materials
    journal, January 2020