Affiliation:
1. School of Chemistry CRANN and AMBER Research Centres Trinity College Dublin College Green Dublin D02 PN40 Ireland
2. Everypixel Media Innovation Group 021 Fillmore St., PMB 15 San Francisco CA 94115 USA
3. Neapolis University Pafos 2 Danais Avenue Pafos 8042 Cyprus
4. Department of Molecular and Cellular Biology Harvard University 52 Oxford St. Cambridge MA 02138 USA
5. Department of Materials Science and Engineering, and Centre for Functional Photonics (CFP) City University of Hong Kong Hong Kong SAR 999077 P. R. China
Abstract
AbstractMachine 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.
Funder
Science Foundation Ireland
Irish Research Council
National Institutes of Health
Subject
Mechanical Engineering,Mechanics of Materials,General Materials Science