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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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A review on machine learning-guided design of energy materials
Abstract The development and design of energy materials are essential for improving the efficiency, sustainability, and durability of energy systems to address climate change issues. However, optimizing and developing energy materials can be challenging due to large and complex search spaces. With the advancements in computational power and algorithms over the past decade, machine learning (ML) techniques are being widely applied in various industrial and research areas for different purposes. The energy material community has increasingly leveraged ML to accelerate property predictions and design processes. This article aims to provide a comprehensive review of research in different energy material fields that employ ML techniques. It begins with foundational concepts and a broad overview of ML applications in energy material research, followed by examples of successful ML applications in energy material design. We also discuss the current challenges of ML in energy material design and our perspectives. Our viewpoint is that ML will be an integral component of energy materials research, but data scarcity, lack of tailored ML algorithms, and challenges in experimentally realizing ML-predicted candidates are major barriers that still need to be overcome.
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- Award ID(s):
- 2332270
- PAR ID:
- 10539559
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Progress in Energy
- Volume:
- 6
- Issue:
- 4
- ISSN:
- 2516-1083
- Format(s):
- Medium: X Size: Article No. 042005
- Size(s):
- Article No. 042005
- Sponsoring Org:
- National Science Foundation
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