Machine-learning for designing nanoarchitectured materials by dealloying
Machine-learning for designing nanoarchitectured materials by dealloying
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DOI：
10.1038/s43246-022-00303-w
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发表时间：
2022-11
影响因子：
7.8
通讯作者：
Chonghang Zhao;Cheng‐Chu Chung;Siying Jiang;M. Noack;Jiun-Han Chen;Kedar Manandhar;J. Lynch;Hui Zhong;wei zhu;Phillip M. Maffettone;D. Olds;M. Fukuto;Ichiro Takeuchi;S. Ghose;T. Caswell;K. Yager;Y. K. Chen‐Wiegart
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文献类型：
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作者：
Chonghang Zhao;Cheng‐Chu Chung;Siying Jiang;M. Noack;Jiun-Han Chen;Kedar Manandhar;J. Lynch;Hui Zhong;wei zhu;Phillip M. Maffettone;D. Olds;M. Fukuto;Ichiro Takeuchi;S. Ghose;T. Caswell;K. Yager;Y. K. Chen‐Wiegart
机器学习-增强材料设计是一种快速开发新材料的新兴方法。它特别适用于设计新的纳米结构材料，其设计参数空间往往很大和复杂。金属脱合金法是一种利用多种元素制备纳米多孔或纳米复合材料的材料设计方法，近年来引起了人们的极大兴趣。在这里，引入了一种机器学习方法来探索金属-试剂脱合金化，从而预测了132个看似合理的三元脱合金系。测试了一个机器学习增强框架，包括通过自动和自主机器学习驱动的同步加速器技术预测脱合金化系统和表征组合薄膜。这项工作展示了利用机器学习增强方法来创建纳米结构薄膜的潜力。
Machine learning-augmented materials design is an emerging method for rapidly developing new materials. It is especially useful for designing new nanoarchitectured materials, whose design parameter space is often large and complex. Metal-agent dealloying, a materials design method for fabricating nanoporous or nanocomposite from a wide range of elements, has attracted significant interest. Here, a machine learning approach is introduced to explore metal-agent dealloying, leading to the prediction of 132 plausible ternary dealloying systems. A machine learning-augmented framework is tested, including predicting dealloying systems and characterizing combinatorial thin films via automated and autonomous machine learning-driven synchrotron techniques. This work demonstrates the potential to utilize machine learning-augmented methods for creating nanoarchitectured thin films.