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
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10302 - Condensed matter physics (including formerly solid state physics, supercond.)
Result continuities
Project
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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