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Estimating DFT calculated voltage using machine learning regression models
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DOI：10.1016/j.matpr.2020.04.204.png)
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摘要
En 中文
The search to improve and optimize properties and structural characterization of materials is a subject of intense scrutiny. However, this has proved challenging because of the complex and preparation-dependent microstructure of many materials. This challenge provides an opportunity to develop, manufacture and deploy materials as fast as possible, in contrast to traditional discovery process, which is complex, expensive, time consuming and labor intensive. In this paper, using materials properties calculated from density functional theory as an input dataset, various machine learning regression methods are explored in order to predict voltages for the battery materials. The results showed that the features are more than capable to predict the voltages of the battery materials. Thus, the developed models proved to have predictive capabilities; with a good accuracy performance. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Materials design and discovery
Density functional theory
Machine learning
Data mining
Optimization
Computational modelling
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论文数:
426
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106
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