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本文引用的文献

1
Localized Plasmonic Structured Illumination Microscopy Using Hybrid Inverse Design.使用混合逆向设计的局部表面等离子体激元结构照明显微术
Nano Lett. 2024 Sep 18;24(37):11581-11589. doi: 10.1021/acs.nanolett.4c03069. Epub 2024 Sep 5.
2
Advancing materials science through next-generation machine learning.通过下一代机器学习推动材料科学发展。
Curr Opin Solid State Mater Sci. 2024 Jun;30. doi: 10.1016/j.cossms.2024.101157. Epub 2024 Apr 3.
3
The Role of Machine Learning in the Understanding and Design of Materials.机器学习在材料理解与设计中的作用。
J Am Chem Soc. 2020 Nov 10;142(48):20273-87. doi: 10.1021/jacs.0c09105.
4
Machine Learning Interatomic Potentials as Emerging Tools for Materials Science.机器学习原子间势：材料科学的新兴工具。
Adv Mater. 2019 Nov;31(46):e1902765. doi: 10.1002/adma.201902765. Epub 2019 Sep 5.
5
Machine learning for molecular and materials science.机器学习在分子和材料科学中的应用。
Nature. 2018 Jul;559(7715):547-555. doi: 10.1038/s41586-018-0337-2. Epub 2018 Jul 25.

Artificial Intelligence and Machine Learning for Materials.

作者信息

Zheng Yuebing

机构信息

Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA.

Materials Science and Engineering Program, Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, USA.

出版信息

Curr Opin Solid State Mater Sci. 2025 Jan;34. doi: 10.1016/j.cossms.2024.101202. Epub 2024 Oct 9.

DOI:10.1016/j.cossms.2024.101202
PMID:40838098
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12363455/
Abstract
摘要