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Updated: Jun 5, 2025

Harmonic Nanoparticles for Regenerative Research

Harmonic Nanoparticles for Regenerative Research

Published on: May 1, 2014

计算机辅助开发新的非线性光学材料

Hongshan Wang¹, Miriding Mutailipu¹, Zhihua Yang¹

¹Research Center for Crystal Materials; State Key Laboratory of Functional Materials and Devices for Special Environmental Conditions; Xinjiang Key Laboratory of Functional Crystal Materials; Xinjiang Technical Institute of Physics & Chemistry, CAS, 40-1 South Beijing Road, Urumqi 830011, China Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China E-mails.

Angewandte Chemie (International ed. in English)

|December 5, 2024

概括

此摘要是机器生成的。

计算机辅助设计加速了新的非线性光学 (NLO) 材料的发现,克服了传统的局限性. 这种方法有效地确定了先进光电应用的有希望的NLO候选人.

关键词:

预测晶体结构的预测第一个原则计算计算.高吞吐量选高吞吐量选机器学习机器学习非线性光学晶体非线性光学晶体

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科学领域:

材料科学材料科学材料科学
凝聚物质物理学凝聚物质物理学
计算化学计算化学

背景情况:

先进的光电技术需要新的非线性光学 (NLO) 材料.
发现NLO材料的传统试错方法是低效和具有挑战性的.
理论预测引导的设计为高效的NLO材料开发提供了一个可行的替代方案.

研究的目的:

审查用于探索新NLO材料的计算机辅助方法.
为了突出计算NLO材料发现的最先进研究.
讨论在各种光谱范围和系统中设计NLO材料的进展.

主要方法:

预测晶体结构和性能计算 (光学和热).
潜在的NLO材料的高通量选,结合机器学习.
针对目标NLO材料开发的计算机辅助设计策略.

主要成果:

通过计算已经确定了许多具有优异光学性能的NLO材料候选者.
在设计深紫外线 (DUV),紫外线 (UV) 和红外线 (IR) NLO 材料方面取得了重大进展.
计算方法在加速NLO材料发现方面的可行性.

结论:

计算机辅助设计对于推进高性能NLO材料至关重要.
这篇综述总结了关键的计算进步和材料系统 (氧化物,化物,化物,化物,化物).
概述了计算NLO材料研究的未来机遇和挑战.