Chemical Composition Optimization of Biocompatible Non-Equiatomic High-Entropy Alloys Using Machine Learning and First-Principles Calculations

Abstract

Obtaining a suitable chemical composition for high-entropy alloys (HEAs) with superior mechanical properties and good biocompatibility is still a formidable challenge through conventional trial-and-error methods. Here, based on a large amount of experimental data, a machine learning technique may be used to establish the relationship between the composition and the mechanical properties of the biocompatible HEAs. Subsequently, first-principles calculations are performed to verify the accuracy of the prediction results from the machine learning model. The predicted Young’s modulus and yield strength of HEAs performed very well in the previous experiments. In addition, the effect on the mechanical properties of alloying an element is investigated in the selected Ti-Zr-Hf-Nb-Ta HEA with the high crystal symmetry. Finally, the Ti8-Zr20-Hf16-Nb35-Ta21 HEA predicted by the machine learning model exhibits a good combination of biocompatibility and mechanical performance, attributed to a significant electron flow and charge recombination. This work reveals the importance of these strategies, combined with machine learning and first-principles calculations, on the development of advanced biocompatible HEAs.

Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

“…The development of modern computer technology has provided superior methods for analyzing and processing the composition of glass artifacts, such as machine learning algorithms [5,6]. Machine learning methods involve multidisciplinary fields including probability theory, statistics, approximation theory, convex analysis, algorithmic complexity theory, and other subjects, which could further enhance our data mining, analysis, and processing capabilities [7,8]. For example, Sun et al [9] conducted a systematic analysis of the glass-forming ability of binary alloys using the support vector machine algorithm to establish the correlation between alloys' composition and properties, and had much success in identifying new materials.…”