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

Joshi, Rajendra P.; Eickholt, Jesse; Li, Liling; Fornari, Marco; Barone, Veronica; Peralta, Juan E.

doi:10.1021/acsami.9b04933

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

Journal Article · Mon Apr 29 00:00:00 EDT 2019 · ACS Applied Materials and Interfaces

DOI:https://doi.org/10.1021/acsami.9b04933· OSTI ID:1574907

Joshi, Rajendra P. ^[1]; Eickholt, Jesse ^[2]; Li, Liling ^[2]; Fornari, Marco ^[3]; Barone, Veronica ^[3];

^[3]

Central Michigan Univ., Mount Pleasant, MI (United States). Dept. of Physics and Science of Advanced Materials Program and Dept. of Computer Science
Central Michigan Univ., Mount Pleasant, MI (United States). Dept. of Computer Science
Central Michigan Univ., Mount Pleasant, MI (United States). Dept. of Physics and Science of Advanced Materials Program

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.

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Research Organization:: Univ. of Southern California, Los Angeles, CA (United States); Central Michigan Univ., Mount Pleasant, MI (United States); Quantum Information Science (QIS)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division; USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0019432

OSTI ID:: 1574907

Alternate ID(s):: OSTI ID: 1777848

Journal Information:: ACS Applied Materials and Interfaces, Vol. 11, Issue 20; ISSN 1944-8244

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 66 works

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25 ENERGY STORAGE
Quantum Information Science (QIS)
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batteries
intercalation electrodes
web tool
voltage predictor
voltage profile diagram

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

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