Advances in machine learning methods in copper alloys: a review

IF 2.1 4区 化学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY Journal of Molecular Modeling Pub Date : 2024-11-12 DOI:10.1007/s00894-024-06177-8
Yingfan Zhang, Shu’e Dang, Huiqin Chen, Hui Li, Juan Chen, Xiaotian Fang, Tenglong Shi, Xuetong Zhu
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引用次数: 0

Abstract

Context

Advanced copper and copper alloys, as significant engineering structural materials, have recently been extensively used in energy, electron, transportation, and aviation domains. Higher requirements urge the emergence of high-performance copper alloys. However, the traditional trial-and-error experimental observations and computational simulation research used to design and develop novel materials are time-consuming and costly. With the accumulation of material research and rapid development of computational ability, the thorough application of material genome engineering has sped up the development of novel materials and facilitates the process of systematic engineering application.

Methods

This review summarizes the benefits of data-driven machine learning techniques and the state of the art of machine learning research in the area of copper alloys. It also displays the widely used computational simulation approaches (e.g., the first-principles calculation, molecular dynamics simulation, phase-field simulations, and finite element analysis) and their combined applications in material design and property prediction. Finally, the limitations of machine learning research methods are outlined, and future development directions are proposed.

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铜合金机器学习方法的进展:综述
背景先进的铜和铜合金作为重要的工程结构材料,近年来已广泛应用于能源、电子、交通和航空领域。更高的要求催生了高性能铜合金的出现。然而,用于设计和开发新型材料的传统试错实验观察和计算模拟研究耗时长、成本高。随着材料研究的不断积累和计算能力的快速发展,材料基因组工程的深入应用加快了新型材料的开发速度,并促进了系统工程应用的进程。它还展示了广泛使用的计算模拟方法(如第一原理计算、分子动力学模拟、相场模拟和有限元分析)及其在材料设计和性能预测中的综合应用。最后,概述了机器学习研究方法的局限性,并提出了未来的发展方向。
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来源期刊
Journal of Molecular Modeling
Journal of Molecular Modeling 化学-化学综合
CiteScore
3.50
自引率
4.50%
发文量
362
审稿时长
2.9 months
期刊介绍: The Journal of Molecular Modeling focuses on "hardcore" modeling, publishing high-quality research and reports. Founded in 1995 as a purely electronic journal, it has adapted its format to include a full-color print edition, and adjusted its aims and scope fit the fast-changing field of molecular modeling, with a particular focus on three-dimensional modeling. Today, the journal covers all aspects of molecular modeling including life science modeling; materials modeling; new methods; and computational chemistry. Topics include computer-aided molecular design; rational drug design, de novo ligand design, receptor modeling and docking; cheminformatics, data analysis, visualization and mining; computational medicinal chemistry; homology modeling; simulation of peptides, DNA and other biopolymers; quantitative structure-activity relationships (QSAR) and ADME-modeling; modeling of biological reaction mechanisms; and combined experimental and computational studies in which calculations play a major role.
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