纳米材料
纳米技术
杠杆(统计)
材料科学
手性(物理)
纳米结构
人工智能
机器学习
计算机科学
物理
手征对称破缺
量子力学
夸克
Nambu–Jona Lasinio模型
作者
Вера Кузнецова,Áine Coogan,Dmitry Botov,Yulia Gromova,Elena V. Ushakova,Yurii K. Gun’ko
标识
DOI:10.1002/adma.202308912
摘要
Abstract Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time‐consuming and labor‐intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis–structure–property–application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.
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