多忠度机器学习用于预测非线性光学晶体的带隙
Zhaoxi Yu1, Pujie Xue1, Bin-Bin Xie2
1Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, P. R. China. lshen@bnu.edu.cn.
Physical chemistry chemical physics : PCCP
|May 28, 2024
概括
一种新的多忠度机器学习 (ML) 方法有效预测非线性光学 (NLO) 晶体带隙. 这种方法克服了数据的局限性,有助于发现新的NLO材料.
科学领域:
- 材料科学 材料科学 材料科学
- 光学是什么?光学是什么?光学是什么?
- 计算化学计算化学
背景情况:
- 非线性光学 (NLO) 材料对于光学和工业的波长转换至关重要.
- 带隙是一个决定NLO晶体性能的关键性质.
- 机器学习 (ML) 显示出预测材料属性的前景,但实验数据稀缺性阻碍了NLO材料发现.
研究的目的:
- 开发一种新的多忠实度ML方法来预测NLO晶体带隙.
- 为了应对在NLO材料勘探中有限的实验数据的挑战.
- 为了提高带隙预测的准确性,用于发现新的NLO材料.
主要方法:
- 提出了一个使用多级描述符和梯度增强回归树的多忠实度ML模型.
- 收集了NLO晶体的计算 (低保真度) 和实验 (高保真度) 带隙.
- 基于化学成分预测的实验带隙,不需要晶体结构信息.
主要成果:
- 多忠实度ML模型在带隙预测准确度方面表现优于单忠实度预测器.
- 在实验带隙测试组中达到0.293 eV的平均绝对误差,而单忠度模型的0.355 eV.
- 观察到低准确度和高准确度标签上的预测准确度之间存在潜在的反向关系.
结论:
- 开发的多忠实度ML模型是用于初步选新型NLO材料的有效计算工具.
- 这种方法表明,尽管数据有限,但有可能加速发现新的NLO材料.
- 进一步的研究可能会改进模型,以获得更高的预测准确度.
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