Discovery of High Entropy Ultra High Temperature Ceramics through Entropy Forming Ability Descriptor and Hume-Rothery rules via machine learning

Abstract

The proposed work aims to develop a data-driven framework assisted with machine learning (ML) based on entropy-forming ability (EFA) descriptor and Hume-Rothery size difference factors to discover high-entropy ultra-high temperature ceramics (HE UHTCs) with exceptional oxidation and mechanical properties for hypersonic environments. Furthermore, the processing-structure-property relationship of the promising HE UHTCs will be established. The HE UHTCs that will be investigated are carbides and borides of the group IVB, VB, and VIB transitional metals (Ti, Zr, Hf, Nb, Ta, Mo, and W) that will be composed of five elements in equimolar or near-equimolar composition with high configurational entropy that promotes the formation of a near single-phase solid solution. The data-driven framework assisted with random forests, as the ML method, will be developed using Python. Data-driven databases (thermodynamic, ab initio, and density-functional theory (DFT)) and HE UHTCs previously reported in the literature will be used to train and validate the model. The promising HE UHTCs will be manufactured through High-Energy Ball Milling and Spark Plasma Sintering sintering. After that, the samples will be subjected to mechanical and oxidation testing. The microstructure and chemical composition will be characterized after manufacturing and testing to establish their processing-structure-property relationship. The data that will be obtained from the microstructure, chemical composition, mechanical and oxidation properties will be fed into the ML model. This study represents a crucial step towards the discovery and tailoring of HE UHTCs for hypersonic environments.

Document Details

Document Type
DoD Grant Award
Publication Date
Mar 06, 2024
Source ID
FA95502310357

Entities

People

  • Alejandra Castellanos

Organizations

  • Air Force Office of Scientific Research
  • United States Air Force
  • University of Texas at El Paso

Tags

Readers

  • Powder metallurgy of Titanium alloys.
  • Quantum Chemistry
  • Surface Engineering/Surface Coating Technology.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Hypersonics