化学
纳米晶
纳米结构
纳米技术
蒙特卡罗方法
密度泛函理论
各向异性
领域(数学)
统计物理学
分子动力学
计算化学
量子力学
纯数学
数学
统计
物理
材料科学
出处
期刊:Chemical Reviews
[American Chemical Society]
日期:2023-03-21
卷期号:123 (7): 4146-4183
被引量:15
标识
DOI:10.1021/acs.chemrev.2c00831
摘要
A significant challenge in the development of functional materials is understanding the growth and transformations of anisotropic colloidal metal nanocrystals. Theory and simulations can aid in the development and understanding of anisotropic nanocrystal syntheses. The focus of this review is on how results from first-principles calculations and classical techniques, such as Monte Carlo and molecular dynamics simulations, have been integrated into multiscale theoretical predictions useful in understanding shape-selective nanocrystal syntheses. Also, examples are discussed in which machine learning has been useful in this field. There are many areas at the frontier in condensed matter theory and simulation that are or could be beneficial in this area and these prospects for future progress are discussed.
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