Predictive analytics for crystalline materials: bulk modulus

Abstract

The machine learning-based generalized model developed for forecasting bulk moduli of various types of stoichiometric and non-stoichiometric crystalline materials. The web application (ThermoEl) deploying the developed predictive model is available for public use.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 01, 2016
Source ID
10.1039/c6ra19284j

Entities

People

  • Alok Choudhary
  • Al’ona Furmanchuk
  • Ankit Agrawal

Organizations

  • Air Force Office of Scientific Research
  • Defense Advanced Research Projects Agency
  • National Institute of Standards and Technology
  • National Science Foundation
  • Northwestern University
  • United States Army
  • United States Department of Energy

Tags

Fields of Study

  • Materials science

Readers

  • Distributed Systems and Data Platform Development
  • Powder metallurgy of Titanium alloys.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks