Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries
| Autor: | Veronica Barone, Jesse Eickholt, Juan E. Peralta, Rajendra P. Joshi, Liling Li, Marco Fornari |
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| Rok vydání: | 2019 |
| Předmět: |
Battery (electricity)
Condensed Matter - Materials Science Electrode material Solid-state chemistry Materials science Artificial neural network Feature vector Materials Science (cond-mat.mtrl-sci) FOS: Physical sciences 02 engineering and technology 010402 general chemistry 021001 nanoscience & nanotechnology 01 natural sciences 0104 chemical sciences Computational science Support vector machine General Materials Science Density functional theory 0210 nano-technology Voltage |
| Zdroj: | ACS Applied Materials & Interfaces. 11:18494-18503 |
| ISSN: | 1944-8252 1944-8244 |
| DOI: | 10.1021/acsami.9b04933 |
| Popis: | 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 5,000 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. 28 pages |
| Databáze: | OpenAIRE |
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