Synergetic Efforts in Automatic Accelerated Materials Design

Abstract

High-throughput first principles methods are to be done to predict the thermal conductivity of materials as a function of composition. In addition the computed elastic tensor data from the AFLOW database and machine learning is to be used for developing models for mechanical properties such as hardness, toughness and malleability. A GUI for the Lucifer Search-API is to be developed. The AFLOW Library of Crystallographic Prototypes is to be expanded and integrated with the AFLOW materials data repository.

Document Details

Document Type
DoD Grant Award
Publication Date
Jan 04, 2017
Source ID
N000141712090

Entities

People

  • Stefano Curtarolo

Organizations

  • Duke University
  • Office of Naval Research
  • United States Navy

Tags

Readers

  • Database Systems and Applications
  • Materials Science and Engineering.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference