Density functional theory and machine learning guided search for RE2Si2O7 with targeted coefficient of thermal expansion

Abstract

Density functional theory (DFT) calculations and machine learning (ML) methods are used to establish a relationship between the crystal structures of rare‐earth (RE) disilicates (RE2Si2O7) and their coefficient of thermal expansion (CTE). The DFT total energy data predict the presence of several energetically competing crystal structures, which is rationalized as one of the reasons for observing polymorphism. An ensemble of support vector regression models is trained to rapidly predict the CTE as a function of RE2Si2O7 crystal chemistry. Experiments subsequently validated the structure and CTE predictions for Sm2Si2O7.

Document Details

Document Type
Pub Defense Publication
Publication Date
May 09, 2020
Source ID
10.1111/jace.17121

Entities

People

  • H.N.G. Wadley
  • Jeroen A. Deijkers
  • Mukil V. Ayyasamy
  • Prasanna V Balachandran

Organizations

  • Office of Naval Research
  • University of Virginia

Tags

Readers

  • Quantum Chemistry
  • Statistical inference.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

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