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Machine Learning Screening of Metal-Ion Battery Electrode Materials

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F21%3AA2202EE3" target="_blank" >RIV/61988987:17310/21:A2202EE3 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1021/acsami.1c04627" target="_blank" >https://doi.org/10.1021/acsami.1c04627</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1021/acsami.1c04627" target="_blank" >10.1021/acsami.1c04627</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Machine Learning Screening of Metal-Ion Battery Electrode Materials

  • Original language description

    Rechargeable batteries provide crucial energy storage systems for renewable energy sources, as well as consumer electronics and electrical vehicles. There are a number of important parameters that determine the suitability of electrode materials for battery applications, such as the average voltage and the maximum specific capacity which contribute to the overall energy density. Another important performance criterion for battery electrode materials is their volume change upon charging and discharging, which contributes to determine the cyclability, Coulombic efficiency, and safety of a battery. In this work, we present deep neural network regression machine learning models (ML), trained on data obtained from the Materials Project database, for predicting average voltages and volume change upon charging and discharging of electrode materials for metal-ion batteries. Our models exhibit good performance as measured by the average mean absolute error obtained from a 10-fold cross-validation, as well as on independent test sets. We further assess the robustness of our ML models by investigating their screening potential beyond the training database. We produce Na-ion electrodes by systematically replacing Li-ions in the original database by Na-ions and, then, selecting a set of 22 electrodes that exhibit a good performance in energy density, as well as small volume variations upon charging and discharging, as predicted by the machine learning model. The ML predictions for these materials are then compared to quantum-mechanics based calculations. Our results reaffirm the significant role of machine learning techniques in the exploration of materials for battery applications.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10302 - Condensed matter physics (including formerly solid state physics, supercond.)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    ACS Applied Materials & Interfaces

  • ISSN

    1944-8244

  • e-ISSN

    1944-8252

  • Volume of the periodical

    13

  • Issue of the periodical within the volume

    45

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    8

  • Pages from-to

    53355-53362

  • UT code for WoS article

    000752870800006

  • EID of the result in the Scopus database

    2-s2.0-85110345806