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High-entropy alloys (HEAs) have attracted considerable attention due to their exceptional properties and outstanding performance across various applications. However, the vast compositional space and complex high-dimensional atomic interactions pose significant challenges in uncovering fundamental physical principles and effectively guiding alloy design. Traditional experimental approaches, often reliant on trial-and-error methods, are time-consuming, cost-prohibitive, and inefficient. To accelerate progress in this field, advanced simulation techniques and data-driven methodologies, particularly machine learning (ML) with a particular interest in nanoscale phenomena, have emerged as transformative tools for composition design, property prediction, and performance optimization. By leveraging extensive materials databases and sophisticated learning algorithms, ML facilitates the discovery of intricate patterns that conventional methods may overlook, and enables the design of HEAs with targeted properties. This review paper provides a comprehensive overview of recent advancements in ML applications for HEAs. It begins with a brief introduction of the fundamental principles of HEAs and ML methodologies, including key algorithms, databases, and evaluation metrics. The critical role of materials representation and feature engineering in ML-driven HEA design is thoroughly discussed. Furthermore, state-of-the-art developments in the integration of ML with HEA research, particularly in composition optimization, property prediction, and phase identification, are systematically reviewed. Special emphasis is placed on cutting-edge deep learning techniques, such as generative models and computer vision, which are revolutionizing the f ield. This study explores the application of machine learning (ML) in developing highly accurate ML interatomic potentials (MLIPs) for molecular dynamics (MD) simulations. These MLIPs have the potential to enhance the accuracy and efficiency of simulations, enabling a more precise representation of the fundamental physics governing high-entropy alloys (HEAs) at the atomic level. A critical discussion is provided, addressing both the potential advantages and the inherent limitations of this approach. This review aims to provide insights into the future directions of ML-driven HEA research, offering a roadmap for advancing material design through data-driven innovation.
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Composition design of high-entropy alloys with deep sets learning
Abstract High entropy alloys (HEAs) are an important material class in the development of next-generation structural materials, but the astronomically large composition space cannot be efficiently explored by experiments or first-principles calculations. Machine learning (ML) methods might address this challenge, but ML of HEAs has been hindered by the scarcity of HEA property data. In this work, the EMTO-CPA method was used to generate a large HEA dataset (spanning a composition space of 14 elements) containing 7086 cubic HEA structures with structural properties, 1911 of which have the complete elastic tensor calculated. The elastic property dataset was used to train a ML model with the Deep Sets architecture. The Deep Sets model has better predictive performance and generalizability compared to other ML models. Association rule mining was applied to the model predictions to describe the compositional dependence of HEA elastic properties and to demonstrate the potential for data-driven alloy design.
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- PAR ID:
- 10366474
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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