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Identifying inorganic solids for harsh environments via machine learning.

Jacob C Hickey^1,2, Arman M Karimaghaei¹, Matt Flores¹

¹Department of Chemistry, University of Houston Texas 77204 USA jbrgoch@central.uh.edu.

Chemical Science

|October 10, 2025

Summary

This summary is machine-generated.

Machine learning models predict material hardness and oxidation resistance, accelerating the discovery of advanced materials for extreme environments. This approach efficiently identifies novel compounds with superior mechanical properties.

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Area of Science:

Materials Science
Computational Materials Science
Chemical Engineering

Background:

Developing materials with high hardness and oxidation resistance is critical for demanding applications in aerospace, defense, and industry.
Traditional materials discovery is often slow and resource-intensive.
Machine learning presents a data-driven, efficient, and scalable alternative for identifying novel materials.

Purpose of the Study:

To develop and validate machine learning models for predicting material hardness and oxidation temperature.
To leverage these models for the discovery of multifunctional materials with combined superior hardness and oxidation resistance.
To establish a robust framework for accelerating materials discovery using artificial intelligence.

Main Methods:

Utilized extreme gradient boosting (XGBoost) models trained on compositional and structural descriptors.
Developed a Vickers hardness (Hv) prediction model using a dataset of 1225 compounds.
Constructed an oxidation temperature (Tp) prediction model using a dataset of 348 compounds.
Validated the oxidation model against 18 diverse inorganic compounds with unmeasured oxidation temperatures.

Main Results:

Successfully developed accurate XGBoost models for predicting Vickers hardness and oxidation temperature.
The oxidation model demonstrated predictive capability on novel inorganic compounds, including borides, silicides, and intermetallics.
Integration of hardness and oxidation models enabled the identification of materials exhibiting both high hardness and enhanced oxidation resistance.

Conclusions:

Machine learning significantly accelerates the discovery of advanced materials for extreme environments.
The developed models provide a powerful framework for identifying multifunctional materials with superior mechanical and thermal properties.
This data-driven approach is crucial for meeting the material demands of aerospace, defense, and industrial sectors.