机器学习引导非线性光学材料的发现（Adv. Theory Simul. 2/2025）

IF 2.9 4区工程技术 Q1 MULTIDISCIPLINARY SCIENCES

Advanced Theory and Simulations Pub Date : 2025-02-21 DOI:10.1002/adts.202570003

Sownyak Mondal, Raheel Hammad

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引用次数: 0

摘要

在文章2400463中，sonnyak Mondal和Raheel Hammad使用预测模型识别新的非线性光学（NLO）材料，使用折射率作为代理。通过密度泛函理论验证了具有最佳硬度和带隙性能的非中心对称材料，成功地确认了几种已建立的NLO材料并增强了固态激光器的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Machine Learning Guided Discovery of Non-Linear Optical Materials (Adv. Theory Simul. 2/2025)

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Machine Learning Guided Discovery of Non-Linear Optical Materials (Adv. Theory Simul. 2/2025)

In article 2400463, Sownyak Mondal and Raheel Hammad use predictive modeling to identify new nonlinear optical (NLO) materials, using refractive index as a proxy. It focuses on non-centrosymmetric materials with optimal hardness and bandgap properties, which are validated through density functional theory, successfully confirming several established NLO materials and enhancing solid-state laser performance.

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来源期刊

Advanced Theory and Simulations Multidisciplinary-Multidisciplinary

CiteScore

5.50

自引率

3.00%

发文量

221

期刊介绍： Advanced Theory and Simulations is an interdisciplinary, international, English-language journal that publishes high-quality scientific results focusing on the development and application of theoretical methods, modeling and simulation approaches in all natural science and medicine areas, including: materials, chemistry, condensed matter physics engineering, energy life science, biology, medicine atmospheric/environmental science, climate science planetary science, astronomy, cosmology method development, numerical methods, statistics