在纳米材料中扩大机器学习的视野,到状纳米结构
Vera Kuznetsova1, Áine Coogan1, Dmitry Botov2,3
1School of Chemistry, CRANN and AMBER Research Centres, Trinity College Dublin, College Green, Dublin, D02 PN40, Ireland.
Advanced materials (Deerfield Beach, Fla.)
|January 19, 2024
概括
机器学习 (ML) 可以加速纳米技术研究,特别是对于性纳米材料. 本综述指导了对奇拉材料的ML应用,突出了它们在光学活性和enantioselectivity方面的潜力.
科学领域:
- 纳米技术纳米技术
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
背景情况:
- 机器学习 (ML) 正通过预测特性和帮助设计,彻底改变纳米材料研究.
- 目前的ML应用主要集中在Achiral纳米材料上,而Chiral纳米材料未被充分研究.
- 状纳米材料具有独特的特性,如高光学活性和选择性,这对于可持续材料开发至关重要.
研究的目的:
- 对阿基拉纳米材料应用的ML方法进行审查.
- 为适应和将这些ML方法扩展到奇拉纳米材料提供指导.
- 通过使用 ML 探索奇拉纳米材料的合成-结构-性质-应用关系.
主要方法:
- 分析现有的ML技术用于无基质纳米材料.
- 审查最近出版的ML应用在奇拉纳米材料的最新出版物.
- 讨论 ML 策略,以了解复杂的性物质关系.
主要成果:
- 鉴定了当前用于奇拉纳米材料的ML应用的局限性.
- 介绍了将ML应用于奇拉纳米材料合成,结构,特性和应用的框架.
- 突出了该领域最近的进展和关键出版物.
结论:
- 机器学习为推进合纳米材料研究和开发提供了巨大的潜力.
- 需要进一步的研究来克服挑战,并充分利用 ML 来实现可持续的合材料.
- 该领域准备迎来显著的增长,有前途的未来应用.
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