Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders
Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders
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DOI：
10.1016/j.actamat.2023.119204
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发表时间：
2023-08
期刊：
影响因子：
9.4
通讯作者：
V. Attari;Danial Khatamsaz;D. Allaire;R. Arróyave
中科院分区：
文献类型：
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作者：
V. Attari;Danial Khatamsaz;D. Allaire;R. Arróyave
集成计算材料工程（ICME）领域结合了广泛的方法来研究材料在一系列长度尺度上的响应。微结构敏感材料设计的一个相对未开发的方面是材料微结构的不确定性传播和量化（UP/UQ），以及建立逆材料设计的过程-结构-性能（PSP）关系。在这项研究中，提出了一种基于改变概率度量的想法的高效UP技术和一种用于基于材料热导率微结构设计的深度生成无监督代表性机器学习方法。概率测度用于表示微观结构空间，Wasserstein度量用于检验UP方法的效率。通过使用深度变分自动编码器（VAE），我们确定的材料/工艺参数和非均质双相微结构的热导率之间的相关性。通过高通量筛选、UP和深度生成的VAE方法，可以通过利用材料的设计空间（重点是微观结构）来揭示过于复杂的PSP关系。作为最后一点，我们证明了生成式机器学习是以微观结构为中心的材料设计的一个有用工具，我们通过研究纳米结构材料中热导率的逆向设计来证明这一点。结果揭示了双相合金的形态、体积分数、特征长度尺度和各相的热扩散率对双相合金热导率的影响。我们的研究结果强调了高通量相场建模和生成式深度学习在连接PSP和以逆微观结构为中心的材料设计方面的优势。
The field of Integrated Computational Materials Engineering (ICME) combines a broad range of methods to study materials’ responses over a spectrum of length scales. A relatively unexplored aspect of microstructure-sensitive materials design is uncertainty propagation and quantification (UP/UQ) of materials’ microstructure, as well as establishing process-structure–property (PSP) relationships for inverse material design. In this study, an efficient UP technique built on the idea of changing probability measures and a deep generative unsupervised representative machine learning method for microstructure-based design of thermal conductivity of materials is proposed. Probability measures are used to represent microstructure space, and Wasserstein metrics are used to test the efficiency of the UP method. By using deep Variational AutoEncoder (VAE), we identify the correlations between the material/process parameters and the thermal conductivity of heterogeneous dual-phase microstructures. Through high-throughput screening, UP, and the deep-generative VAE method, PSP relationships that are too complex can be revealed by exploiting the materials’ design space with an emphasis on microstructures. As a last point, we demonstrate generative machine learning serves as a useful tool for inverse microstructure-centered materials design, and we demonstrate this by examining the inverse design of thermal conductivity in nano-structured materials. The results reveal the effects of morphology, volume fraction, characteristic length scale, and the individual thermal diffusivity of phases on the thermal conductivity of dual-phase alloys. Our findings emphasize the advantages of high-throughput phase-field modeling and generative deep learning for linking PSP and inverse microstructure-centered materials design.