Machine learning in high-entropy alloys: phase formation predictions with artificial neural networks

Abstract

Due to their complex compositions, high entropy alloys (HEAs) offer a diverse range of material properties, making them highly adaptable for various applications, including those crucial for future sustainability. Phase engineering in HEAs presents a unique opportunity to tailor materials for environmentally friendly technologies and energy-efficient solutions. However, the challenge of predicting phase selection, a key aspect in harnessing the full potential of HEAs for sustainable applications, is compounded by the limited availability of HEA data. This study presents a distinctive approach by using a precisely produced and selected dataset to train an artificial neural network (ANN) model. This dataset, unlike prior studies, is uniquely constructed to contain an equal amount of training data for each phase in HEAs, which includes single-phase solid solutions (SS), amorphous (AM), and intermetallic compounds (IM). This methodology is relatively unexplored in the field and addresses the imbalanced data issue common in HEA research. To accurately assess the model's performance, rigorous cross-validation was employed to systematically adapt the model's hyperparameters for phase formation prediction. The assessment includes metrics such as phase-wise accuracy (AM 86.67% SS 81.25% & IM 82.35%), confusion matrix, and Micro-F1 score (0.83), all of which collectively demonstrate the effectiveness of this approach. The study highlights the importance of feature parameters in phase prediction for HEAs, shedding light on the factors influencing phase selection. Its balanced dataset and training method notably advance machine learning in HEA phase prediction, providing valuable insights for material design amidst challenges and data scarcity in the field.

Unlocking the potential of open-tunnel oxides: DFT-guided design and machine learning-enhanced discovery for next-generation industry-scale battery technologies

“…In recent years, deep learning algorithms such as graph neural networks (GNNs) have surfaced as a cost-effective avenue for predicting formation energy and band gap while establishing correlations between structure and properties. 54–57 Compared to traditional methods, machine learning (ML) boasts two distinct advantages: compactness and the ability to predict the properties of numerous compounds within seconds without requiring substantial processing power. ML relies exclusively on information garnered from DFT and experimental techniques.…”