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Adaptive Strategies for Materials Design using Uncertainties.

Prasanna V Balachandran1, Dezhen Xue1,2, James Theiler3

  • 1Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.

Scientific Reports
|January 22, 2016
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Summary
This summary is machine-generated.

Adaptive design strategies accelerate materials discovery by using machine learning to predict elastic properties. Incorporating prediction uncertainty in selectors significantly improves the efficiency of finding new materials with desired bulk, shear, and Young

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Machine Learning in Materials Discovery

Background:

  • Discovering new materials with specific elastic properties is crucial for technological advancement.
  • Traditional materials discovery is often time-consuming and resource-intensive.
  • Adaptive design strategies offer a promising approach to accelerate this process.

Purpose of the Study:

  • To compare various adaptive design strategies for efficient materials discovery.
  • To investigate the impact of data set size, machine learning regressors, and selection methods on design efficiency.
  • To identify optimal strategies for finding materials with desired elastic properties.

Main Methods:

  • Utilized a dataset of 223 M2AX family compounds with pre-computed elastic properties (bulk, shear, Young's modulus) via density functional theory.
  • Employed an iterative machine learning approach: training regressors to predict elastic properties from elemental orbital radii.
  • Implemented selectors that leverage predictions and their uncertainties to guide the selection of next materials for investigation.

Main Results:

  • Selectors incorporating prediction uncertainty demonstrated superior performance compared to those that did not.
  • The choice of data set size, regressor, and selector significantly impacts the efficiency of the adaptive design process.
  • Demonstrated the effectiveness of machine learning-guided adaptive design for materials property prediction.

Conclusions:

  • Adaptive design strategies, particularly those utilizing prediction uncertainty, can significantly accelerate the search for new materials.
  • This study provides a framework for guiding the exploration of materials with targeted elastic properties.
  • Highlights the potential of computational tools in optimizing materials design and discovery workflows.