Machine Learning Prediction for Bandgaps of Inorganic Materials

Lang Wu, Yue Xiao, Mithun Ghosh, Qiang Zhou, Qing Hao

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Machine learning approaches are explored to predict the bandgaps of inorganic compounds using known compositional features, based on a dataset of 3896 compounds with experimentally measured bandgaps. In particular,among various existing methods, we propose a new method, random forest with Gaussian process model as leaf nodes (RF-GP), and show its advantages. We have also investigated ensemble learning methods, which produce superior results overother traditional machine learning methods, but at the cost of extra computational load and further reduced interpretability.

Original languageEnglish (US)
Pages (from-to)34-39
Number of pages6
JournalES Materials and Manufacturing
StatePublished - Sep 2020

ASJC Scopus subject areas

  • Ceramics and Composites
  • Metals and Alloys
  • Applied Mathematics
  • Polymers and Plastics
  • Numerical Analysis
  • Building and Construction
  • Modeling and Simulation


Dive into the research topics of 'Machine Learning Prediction for Bandgaps of Inorganic Materials'. Together they form a unique fingerprint.

Cite this