Recent achievements of the team - Deep Learning-Based Framework for Efficient Design of Multicomponent High Hardness High Entropy Alloys
Our team published the paper “Deep Learning-Based Framework for Efficient Design of Multicomponent High Hardness High Entropy Alloys” in the journal ACS Applied Materials & Interfaces (IF: 8.3, CAS Region II). The School of Computer Engineering and Science of Shanghai University is the first department of the paper, with Yuexing Han as the first author, Hui Wang as the second author, and Yi Liu as the corresponding author.
In the field of materials science, high-entropy alloys (HEAs) have become a research hotspot due to their excellent properties. However, finding an optimal design that is both innovative and reliable in the vast alloy composition space poses a huge challenge. Traditional trial-and-error methods are inefficient, while purely data-driven approaches struggle to ensure the practical performance of designs. To address this issue, we propose a deep learning-based framework that combines domain knowledge in materials science with data-driven techniques to optimize the design process of multicomponent high-hardness high-entropy alloys.
First, we developed a Materials Cascade Embedding (MCE) module and integrated it with the BiLSTM-CRF network (MCE-BILSTM-CRF) to automatically analyze 2,698 papers published in the past 5 years and extracted 8,067 data points. By incorporating materials domain knowledge into the data analysis, we identified high-potential elements and critical process conditions to guide the design and construction of machine learning datasets. After manually summarizing and organizing the target literature, we constructed a hardness dataset containing 13 elements. Based on this, we utilize a two-stage design strategy combining genetic algorithm (GA) and particle swarm optimization (PSO) to develop multi-component high-entropy alloys. The first stage explores the alloy system and the second stage optimizes the component ratios, thereby promoting innovation and performance enhancement. Our analysis combines SHAP feature significance and Pearson correlation coefficients (PCCs), complemented by materials domain knowledge, to validate the findings and guide the selection of alloy systems. In the end, we successfully designed three high-entropy alloys that differed from the existing dataset and predicted an average relative hardness error of less than 5%, with the optimal alloy being only 38 HV lower than the historical record.
