Accelerated Discovery of Compositionally Complex Alloys for Direct Thermal Energy Conversion

Project: Research project

Description

Rational, data-driven materials discovery would be an immense boon for research and development, making these efforts far faster and cheaper. In such a paradigm, computer models trained to find patterns in massive chemical datasets would rapidly scan compositions and systematically identify attractive candidates for technological applications, such as the materials of this proposal, namely high entropy alloys, thermoelectrics, etc. Such predictive models would thus replace Edisonian trial-and-error in the laboratory by focusing further experimental studies on only the most promising materials.

Computational materials discovery, specifically that based on first-principles density functional theory (DFT) techniques can be thought of consisting of (at least) four separate tasks:
A) Data construction of properties for many known materials/structures
B) Additional of information for unknown structures
C) Data mining or machine learning on dataset to extract patterns in data
D) Discovery of new compounds and materials

This proposal involves all four aspects of this sequence, especially A, C, and D.
StatusActive
Effective start/end date9/23/193/31/22

Funding

  • Stanford University, SLAC National Accelerator Laboratory (197455//DE-AC02-76SFOOS 15)
  • Department of Energy (197455//DE-AC02-76SFOOS 15)

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Thermal energy
Energy conversion
Density functional theory
Data mining
Learning systems
Entropy
Chemical analysis