Predictive analytics for crystalline materials: Bulk modulus

Al'Ona Furmanchuk, Ankit Agrawal*, Alok Choudhary

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

The bulk modulus is one of the important parameters for designing advanced high-performance and thermoelectric materials. The current work is the first attempt to develop a generalized model for forecasting bulk moduli of various types of crystalline materials, based on ensemble predictive learning using a unique set of attributes. The attributes used are a combination of experimentally measured structural details of the material and chemical/physical properties of atoms. The model was trained on a data set of stoichiometric compounds calculated using density functional theory (DFT). It showed good predictive performance when tested against external DFT-calculated and experimentally measured stoichiometric and non-stoichiometric materials. The generalized model found correlations between bulk modulus and features defining bulk modulus in specific families of materials. The web application (ThermoEl) deploying the developed predictive model is available for public use.

Original languageEnglish (US)
Pages (from-to)95246-95251
Number of pages6
JournalRSC Advances
Volume6
Issue number97
DOIs
StatePublished - 2016

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)

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