Prediction of Pressure-Composition-Temperature Curves of AB2-Type Hydrogen Storage Alloys by Machine Learning

Jeong Min Kim, Taejun Ha, Joonho Lee, Young Su Lee, Jae Hyeok Shim

Research output: Contribution to journalArticlepeer-review

Abstract

Pressure-composition-temperature (PCT) curves for hydrogen absorption and desorption of AB2-type hydrogen storage alloys at arbitrary temperatures are predicted by three machine learning models such as random forest, K-nearest neighbor and deep neural network (DNN). Two data generation methods are adopted to increase the number of data points. A new form of the PCT curve functions is suggested to fit experimental data, which greatly helps improve the prediction accuracy. A van’t Hoff type equation is used to generate unmeasured temperature data, which improves the model performance on the PCT behavior at various temperatures. The results indicate that a DNN is the best model for predicting the PCT behavior with a high average correlation value R2 = 0.93070. Graphical Abstract: [Figure not available: see fulltext.]

Original languageEnglish
JournalMetals and Materials International
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Deep neural network
  • Hydrogen sorption
  • Hydrogen storage alloy
  • Machine learning
  • Pressure-composition-temperature curve

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Mechanics of Materials
  • Metals and Alloys
  • Materials Chemistry

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