面向多主元素合金的多目标优化生成对抗设计

IF 2.4 3区 材料科学 Q3 ENGINEERING, MANUFACTURING
Z. Li, N. Birbilis
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引用次数: 0

摘要

新型合金(如多元素合金,包括所谓的高熵合金)的发现对于技术进步仍然至关重要。多主元合金可以表现出独特的良好机械性能,但其成分的复杂性导致其设计和性能的理解具有挑战性。随着材料基因组概念的出现,人们有可能利用计算设计方法来研究新型材料。然而,这种设计的复杂性往往需要巨大的计算能力和复杂的数据分析。为了解决这个问题,我们引入了一个新框架,即基于非优势排序优化的生成对抗网络(NSGAN),用于发现和探索新型 MPEA。通过利用遗传算法和生成对抗网络(GAN)的强大功能,NSGAN 为合金设计中的高维多目标优化挑战提供了有效的解决方案。该框架可根据特定合金特性生成 MPEA。此外,一个在线网络工具/软件应用 NSGAN 框架,向更广泛的科学领域传播该方法(同时提供支持代码)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Multi-objective Optimization-Oriented Generative Adversarial Design for Multi-principal Element Alloys

Multi-objective Optimization-Oriented Generative Adversarial Design for Multi-principal Element Alloys

The discovery of novel alloys, such as multi-principal element alloys (MPEAs)—inclusive of the so-called high-entropy alloys—remains essential for technological advancement. Multi-principal element alloys can manifest uniquely favorable mechanical properties, but the complexity of their compositions results in their design and performance being challenging to understand. With the emergence of the materials genome concept, there is potential to pursue novel materials using computational design approaches. However, the complexity of such design often requires immense computational power and sophisticated data analysis. In an attempt to address this, we introduce the application of a new framework, the non-dominant sorting optimization-based generative adversarial networks (NSGAN) in the discovery and exploration of novel MPEAs. By harnessing the power of genetic algorithms and generative adversarial networks (GANs), NSGANs offer an effective solution for high-dimensional multi-objective optimization challenges in alloy design. The framework is demonstrated to generate MPEAs according to specific alloy properties. Furthermore, an online web tool/software applies the NSGAN framework to disseminate the methodology to the broader scientific arena (along with the supporting code made available).

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来源期刊
Integrating Materials and Manufacturing Innovation
Integrating Materials and Manufacturing Innovation Engineering-Industrial and Manufacturing Engineering
CiteScore
5.30
自引率
9.10%
发文量
42
审稿时长
39 days
期刊介绍: The journal will publish: Research that supports building a model-based definition of materials and processes that is compatible with model-based engineering design processes and multidisciplinary design optimization; Descriptions of novel experimental or computational tools or data analysis techniques, and their application, that are to be used for ICME; Best practices in verification and validation of computational tools, sensitivity analysis, uncertainty quantification, and data management, as well as standards and protocols for software integration and exchange of data; In-depth descriptions of data, databases, and database tools; Detailed case studies on efforts, and their impact, that integrate experiment and computation to solve an enduring engineering problem in materials and manufacturing.
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