Accelerated discovery of eutectic compositionally complex alloys by generative machine learning

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL npj Computational Materials Pub Date : 2024-09-03 DOI:10.1038/s41524-024-01385-5
Z. Q. Chen, Y. H. Shang, X. D. Liu, Y. Yang
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Abstract

Eutectic alloys have garnered significant attention due to their promising mechanical and physical properties, as well as their technological relevance. However, the discovery of eutectic compositionally complex alloys (ECCAs) (e.g. high entropy eutectic alloys) remains a formidable challenge in the vast and intricate compositional space, primarily due to the absence of readily available phase diagrams. To address this issue, we have developed an explainable machine learning (ML) framework that integrates conditional variational autoencoder (CVAE) and artificial neutral network (ANN) models, enabling direct generation of ECCAs. To overcome the prevalent problem of data imbalance encountered in data-driven ECCA design, we have incorporated thermodynamics-derived data descriptors and employed K-means clustering methods for effective data pre-processing. Leveraging our ML framework, we have successfully discovered dual- or even tri-phased ECCAs, spanning from quaternary to senary alloy systems, which have not been previously reported in the literature. These findings hold great promise and indicate that our ML framework can play a pivotal role in accelerating the discovery of technologically significant ECCAs.

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通过生成式机器学习加速发现共晶成分复杂的合金
共晶合金因其良好的机械和物理特性及其技术相关性而备受关注。然而,在广阔而错综复杂的成分空间中,发现共晶成分复杂合金(ECCA)(如高熵共晶合金)仍然是一项艰巨的挑战,这主要是由于缺乏现成的相图。为解决这一问题,我们开发了一种可解释的机器学习(ML)框架,该框架集成了条件变异自动编码器(CVAE)和人工中性网络(ANN)模型,可直接生成 ECCA。为了克服数据驱动 ECCA 设计中普遍遇到的数据不平衡问题,我们纳入了热力学衍生数据描述符,并采用 K-means 聚类方法进行有效的数据预处理。利用我们的 ML 框架,我们成功地发现了从四元合金系统到三元合金系统的双相甚至三相 ECCA,这在以前的文献中从未报道过。这些发现前景广阔,表明我们的 ML 框架可以在加速发现具有重要技术意义的 ECCA 方面发挥关键作用。
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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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