# Accelerating High-Entropy Alloy Design via Machine Learning: Predicting Yield Strength from Composition

## TL;DR

A machine learning model predicts the strength of high-entropy alloys from their composition, speeding up material design and reducing costly experiments.

## Contribution

A Gradient Boosting model with 0.85 R2 predicts HEA yield strength, enabling efficient alloy design and resource savings.

## Key findings

- Gradient Boosting achieved an R2 score of 0.85 in predicting HEA yield strength.
- The model accurately captured yield-strength trends across diverse HEA categories.
- The approach reduces trial-and-error experiments in HEA design.

## Abstract

What are the main findings?
Developed a machine learning model predicting HEA yield strength from composition.Gradient Boosting achieved the best performance with an R2 of 0.85.Model captures experimental yield-strength trends across diverse HEA categories.

Developed a machine learning model predicting HEA yield strength from composition.

Gradient Boosting achieved the best performance with an R2 of 0.85.

Model captures experimental yield-strength trends across diverse HEA categories.

What are the implications of the main findings?
Enables fast screening of HEA compositions with targeted high yield strength.Reduces trial-and-error experiments, saving resources and energy in HEA design.Offers a general framework extendable to other mechanical properties of HEAs.

Enables fast screening of HEA compositions with targeted high yield strength.

Reduces trial-and-error experiments, saving resources and energy in HEA design.

Offers a general framework extendable to other mechanical properties of HEAs.

High-entropy alloys (HEAs) have attracted significant attention due to their exceptional physical, chemical, and mechanical properties. The current development of HEAs primarily depends on time-consuming and costly trial-and-error approaches, which not only hinder the efficient exploration of new compositions but also result in unnecessary resource and energy consumption, thereby negatively affecting sustainable development and production. To address this challenge, this study introduces a machine learning-based methodology for predicting the yield strengths of various HEA compositions. The model was trained using 181 data points and achieved an R2 performance score of 0.85. To further assess its reliability and generalization capability, the model was validated using external data not included in the collected dataset. The validation was performed across four categories: modified Cantor alloys, refractory HEAs, eutectic HEAs, and other HEAs. The predicted yield strength trends were found to align with the actual experimental trends, demonstrating the model’s robust performance across various categories of HEAs. The proposed machine learning approach is expected to facilitate the combinatorial design of HEAs, thereby enabling efficient optimization of compositions and accelerating the development of novel alloys. Moreover, it has the potential to serve as a guideline for sustainable alloy design and environmentally conscious production in future HEA development.