Informatics Aided Design for Alloys

Abstract

We have developed a methodology that combines computational materials science with statistical learning to rapidly, economically and yet robustly identify key parameters that govern structure-property relationships across length scales. Based on this approach, we have demonstrated, using specific alloy chemistry platforms, practical ways by which information based on designers' needs can be efficiently linked to fundamental materials characteristics. This can provide a strategy for accelerating the identification of promising materials chemistries that will meet the complexity of design parameters. In this manner, expensive and complex experiments and computations need be targeted to only those materials that show the best promise.

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Document Details

Document Type
Technical Report
Publication Date
Feb 28, 2009
Accession Number
ADA589784

Entities

People

  • Krishna Rajan

Organizations

  • Iowa State University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Chemistry
  • Crystal Chemistry
  • Crystal Structure
  • Crystallography
  • Data Mining
  • Genetic Algorithms
  • Heat Resistant Alloys
  • High Temperature
  • Identification
  • Information Science
  • Materials
  • Materials Engineering
  • Materials Science
  • Materials Testing
  • Neural Networks
  • Statistical Analysis

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Defense Technology Research and Development.
  • Strategic Security Studies