Machine learning-based prediction of phases in high-entropy alloys: A data article

Data-driven analysis and prediction of stable phases for high-entropy alloy design

High-entropy alloys (HEAs) represent a promising class of materials with exceptional structural and functional properties. However, their design and optimization pose challenges due to the large composition-phase space coupled with the complex and diverse nature of the phase formation dynamics. In this study, a data-driven approach that utilizes machine learning (ML) techniques to predict HEA phases and their composition-dependent phases is proposed. By employing a comprehensive dataset comprising 5692 experimental records encompassing 50 elements and 11 phase categories, we compare the performance of various ML models. Our analysis identifies the most influential features for accurate phase prediction. Furthermore, the class imbalance is addressed by employing data augmentation methods, raising the number of records to 1500 in each category, and ensuring a balanced representation of phase categories. The results show that XGBoost and Random Forest consistently outperform the other models, achieving 86% accuracy in predicting all phases. Additionally, this work provides an extensive analysis of HEA phase formers, showing the contributions of elements and features to the presence of specific phases. We also examine the impact of including different phases on ML model accuracy and feature significance. Notably, the findings underscore the need for ML model selection based on specific applications and desired predictions, as feature importance varies across models and phases. This study significantly advances the understanding of HEA phase formation, enabling targeted alloy design and fostering progress in the field of materials science.

Swarm Smart Meta-Estimator for 2D/2D Heterostructure Design

Two-dimensional (2D) semiconductors are central to many scientific fields. The combination of two semiconductors (heterostructure) is a good way to lift many technological deadlocks. Although ab initio calculations are useful to study physical properties of these composites, their application is limited to few heterostructure samples. Herein, we use machine learning to predict key characteristics of 2D materials to select relevant candidates for heterostructure building. First, a label space is created with engineered labels relating to atomic charge and ion spatial distribution. Then, a meta-estimator is designed to predict label values of heterostructure samples having a defined band alignment (descriptor). To this end, independently trained k-nearest neighbors (KNN) regression models are combined to boost the regression. Then, swarm intelligence principles are used, along with the boosted estimator's results, to further refine the regression. This new "swarm smart" algorithm is a powerful and versatile tool to select, among experimentally existing, computationally studied, and not yet discovered van der Waals heterostructures, the most likely candidate materials to face the scientific challenges ahead.

Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization

High-entropy alloys (HEAs) with high hardness and high ductility can be considered as candidates for wear-resistant applications. However, designing novel HEAs with multiple desired properties using traditional alloy design methods remains challenging due to the enormous composition space. In this work, we proposed a machine-learning-based framework to design HEAs with high Vickers hardness (H) and high compressive fracture strain (D). Initially, we constructed data sets containing 172,467 data with 161 features for D and H, respectively. Four-step feature selection was performed, with the selection of 12 and 8 features for the D and H prediction models based on the optimal algorithms of the support vector machine (SVR) and light gradient boosting machine (LightGBM), respectively. The R2 of the well-trained models reached 0.76 and 0.90 for the 10-fold cross validation. Nondominated sorting genetic algorithm version II (NSGA-II) and virtual screening were employed to search for the optimal alloying compositions, and four recommended candidates were synthesized to validate our methods. Notably, the D of three candidates have shown significant improvements compared to the samples with similar H in the original data sets, with increases of 135.8, 282.4, and 194.1% respectively. Analyzing the candidates, we have recommended suitable atomic percentage ranges for elements such as Al (2-14.8 at %), Nb (4-25 at %), and Mo (3-9.9 at %) in order to design HEAs with high hardness and ductility.

Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook

Machine learning (ML) has emerged as a powerful tool in the research field of high entropy compounds (HECs), which have gained worldwide attention due to their vast compositional space and abundant regulatability. However, the complex structure space of HEC poses challenges to traditional experimental and computational approaches, necessitating the adoption of machine learning. Microscopically, machine learning can model the Hamiltonian of the HEC system, enabling atomic-level property investigations, while macroscopically, it can analyze macroscopic material characteristics such as hardness, melting point, and ductility. Various machine learning algorithms, both traditional methods and deep neural networks, can be employed in HEC research. Comprehensive and accurate data collection, feature engineering, and model training and selection through cross-validation are crucial for establishing excellent ML models. ML also holds promise in analyzing phase structures and stability, constructing potentials in simulations, and facilitating the design of functional materials. Although some domains, such as magnetic and device materials, still require further exploration, machine learning's potential in HEC research is substantial. Consequently, machine learning has become an indispensable tool in understanding and exploiting the capabilities of HEC, serving as the foundation for the new paradigm of Artificial-intelligence-assisted material exploration.

Phase Prediction Study of High-Entropy Energy Alloy Generation Based on Machine Learning

Traditional energy sources such as fossil fuels can cause environmental pollution on the one hand, and on the other hand, there will be a shortage of diminishing stocks. Recently, a variety of new energy sources have been proposed by scientists, such as nuclear energy, hydrogen energy, wind energy, water energy, and solar energy. There are already many technologies for converting and storing energy generated from new energy systems, such as various storage batteries. One of the keys to the commercialization of these new energy sources is to explore new materials. Researchers have performed a lot of research on new energy material preparation, mechanical properties, radiation resistance, energy storage, etc. However, new energy metal materials are still unable to combine radiation resistance, good mechanical properties, excellent energy storage, and other characteristics. There is still a lack of breakthrough materials with better performance or more stable structure. Recently, researchers have discovered that high-entropy alloys have become one of the most promising new energy metal materials. Because it not only has high energy storage and high strength, but also has high stability and high radiation resistance, and is easy to form a simple phase, the prediction of phases in high-entropy energy alloys is very critical, and the generation of designed phases in high-entropy energy alloys is a very important step. In this study, three machine learning algorithms were used to predict the generated phase classification in high-entropy alloys, namely, support-vector machine (SVM) model, decision tree (DT) model, and random forest (RF) model. The models are optimized by grid search methods and cross-validated, and performance was evaluated with the aim of significantly improving the accuracy of generative phase prediction, and the results show that the random forest algorithm has the best prediction ability, reaching 0.93 prediction accuracy. The ROC (receiver operating characteristic) curve of the model shows that the random forest algorithm has the best classification of solid-solution (SS) phases, where the classification probabilities AUC (area under the curve) area for amorphous phase (AM), intermetallic phase (IM), and solid-solution phase (SS), respectively, are 0.95, 0.96, and 1, respectively, , which can predict the generated phases of high-entropy energy alloys well.

Automatic Featurization Aided Data-Driven Method for Estimating the Presence of Intermetallic Phase in Multi-Principal Element Alloys

Multi-principal element alloys (MPEAs) are characterized by a high-dimensional materials design space, and data-driven models can be considered as the best tools to describe the structure–property relationship in this class of materials. Predicting the prevalence of an intermetallic (IM) phase in a high-entropy alloy (HEA) regime of MPEAs has become a very important research direction recently. In this work, Automatic Featurization capability has been deployed computationally to extract composition and property features from the datasets of MPEAs. Data visualization has been performed, and through principal component analysis, the relative impacts of the input features on the two principal components have been specified. Artificial neural network is then trained upon the set of compostion, property and phase information features. A GUI interface is subsequently developed on top of the prediction model to enable the user-friendly computer environment for detection of the IM phase in a compositionally complex alloy.

Machine Learning Approach to Community Detection in a High-Entropy Alloy Interaction Network

There is a growing trend toward the use of interaction network methods and algorithms, including community-based detection methods, in various fields of science. The approach is already used in many applications, for example, in social sciences and health informatics to analyze behavioral patterns during the COVID-19 pandemic, protein-protein networks in biological sciences, agricultural science, economy, and so forth. This paper attempts to build interaction networks based on high-entropy alloy (HEA) descriptors in order to discover HEA communities with similar functionality. In addition, these communities could be leveraged to discover new alloys not yet included in the data set without any experimental laboratory effort. This research has been carried out using two community detection algorithms, the Louvain algorithm and the enhanced particle swarm optimization (PSO) algorithm. The data set, which is used in this paper, includes 90 HEAs and 6 descriptors. The results reveal 13 alloy communities, and the accuracy of the results is validated by the modularity. The experimental results show that the method with the PSO-based community detection algorithm can achieve alloy communities with an average accuracy improvement of 0.26 compared to the Louvain algorithm. Furthermore, some characteristics of HEAs, for example, their phase composition, could be predicted by the extracted communities. Also, the HEA phase composition has been predicted by the proposed method and achieved about 93% precision.

Modeling environment-dependent atomic-level properties in complex-concentrated alloys

Complex-concentrated-alloys (CCAs) are of interest for a range of applications due to a host of desirable properties, including high-temperature strength and tolerance to radiation damage. Their multi-principal component nature results in a vast number of possible atomic environments with the associated variability in chemistry and structure. This atomic-level variability is central to the unique properties of these alloys but makes their modeling challenging. We combine atomistic simulations using many body potentials with machine learning to develop predictive models of various atomic properties of CrFeCoNiCu-based CCAs: relaxed vacancy formation energy, atomic-level cohesive energy, pressure, and volume. A fingerprint of the local atomic environments is obtained combining invariants associated with the local atomic geometry and periodic-table information of the atoms involved. Importantly, all descriptors are based on the unrelaxed atomic structure; thus, they are computationally inexpensive to compute. This enables the incorporation of these models into macroscopic simulations. The models show good accuracy and we explore their ability to extrapolate to compositions and elements not used during training.

Enhance Teaching-Learning-Based Optimization for Tsallis-Entropy-Based Feature Selection Classification Approach

Feature selection is an effective method to reduce the number of data features, which boosts classification performance in machine learning. This paper uses the Tsallis-entropy-based feature selection to detect the significant feature. Support Vector Machine (SVM) is adopted as the classifier for classification purposes in this paper. We proposed an enhanced Teaching-Learning-Based Optimization (ETLBO) to optimize the SVM and Tsallis entropy parameters to improve classification accuracy. The adaptive weight strategy and Kent chaotic map are used to enhance the optimal ability of the traditional TLBO. The proposed method aims to avoid the main weaknesses of the original TLBO, which is trapped in local optimal and unbalance between the search mechanisms. Experiments based on 16 classical datasets are selected to test the performance of the ETLBO, and the results are compared with other well-established optimization algorithms. The obtained results illustrate that the proposed method has better performance in classification accuracy.

Machine Learning Accelerated, High Throughput, Multi-Objective Optimization of Multiprincipal Element Alloys

Multiprincipal element alloys (MPEAs) have gained surging interest due to their exceptional properties unprecedented in traditional alloys. However, identifying an MPEA with desired properties from a huge compositional space via a cost-effective design remains a grand challenge. To address this challenge, the authors present a highly efficient design strategy of MPEAs through a coherent integration of molecular dynamics (MD) simulation, machine learning (ML) algorithms, and genetic algorithm (GA). The ML model can be effectively trained from 54 MD simulations to predict the stiffness and critical resolved shear stress (CRSS) of CoNiCrFeMn alloys with a relative error of 2.77% and 2.17%, respectively, with a 12 600-fold reduction of computation time. Furthermore, by combining the highly efficient ML model and a multi-objective GA, one can predict 100 optimal compositions of CoNiCrFeMn alloys with simultaneous high stiffness and CRSS, as verified by 100 000 ML-accelerated predictions. The highly efficient and precise design strategy can be readily adapted to identify MPEAs of other principal elements and thus substantially accelerate the discovery of other high-performance MPEA materials.