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机器学习预测非线性光学晶体和多态晶体的双重断裂,具有不同的双重断裂活动

Ding Peng¹, Zhaoxi Yu¹, Sangen Zhao²

¹Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.

The journal of physical chemistry letters

|June 9, 2025

概括

这项研究介绍了hp-wACSFs,这是预测晶体双折射的新描述符. 它可以识别具有不同双断活动的多态体,从而推进非线性光学材料的发现.

科学领域:

材料科学材料科学材料科学
晶体学晶体学是指结晶学.
计算化学计算化学

背景情况:

非线性光学 (NLO) 晶体对于激光技术至关重要,双断率是关键性质.
通过材料修改优化双断率对于先进的激光应用是必不可少的.
机器学习 (ML) 在预测NLO材料属性方面表现有前途,但识别具有明显双折射的多态仍然具有挑战性.

研究的目的:

开发一种新的描述器,hp-wACSFs,用于预测无机晶体的双折射.
构建和评估ML分类器,以识别具有双断活性的NLO晶体和具有不同双断性的多态.
执行虚拟选,以发现具有可调的双折射的新多态.

主要方法:

开发hp-wACSFs,一种基于原子中心对称函数的新描述符.
用于两个预测任务的ML分类器的构建和应用:多形态中的双折射活动和差异双折射.
使用开发的ML模型实施虚拟选.

主要成果:

hp-wACSFs描述器的性能与以前用于预测一般双断的方法相比.
机器学习模型成功地识别了具有不同双断活动的多态体,在这个具有挑战性的任务中达到0.8的准确性.
虚拟选识别了具有明显双折特性的潜在多态体.

结论:

hp-wACSFs是一个有效的描述器,用于预测无机晶体的双断.
开发的ML方法有助于识别具有多种双断度的多态体,这对于NLO应用至关重要.
这项工作有助于发现具有定制光学特性的新型NLO材料.

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