均方误差
梯度升压
理论(学习稳定性)
随机森林
Boosting(机器学习)
Atom(片上系统)
非线性系统
支持向量机
均方根
非线性回归
算法
计算机科学
回归分析
人工智能
机器学习
数学
统计
物理
量子力学
嵌入式系统
作者
Ai Wang,Yaohui Yin,Z. Sun,Guangyong Jin,Chao Xin
标识
DOI:10.1002/adts.202400048
摘要
Abstract Nonlinear optical crystals (NLO) are a key class of functional materials in the field of laser technology due to their excellent frequency conversion effects and physical–chemical stability. The research aims to find NLO crystals with superior stability by predicting their formation energy. In this study, only compositional information is utilized as input features and models are constructed using regression algorithms such as Random Forest Regression (RFR), Support Vector Regression (SVR), and Gradient Boosting Regression (GBR). Notably, the GBR model exhibited outstanding predictive performance, with an R 2 value of 0.935 and root mean square error ( RMSE ) of 0.248 eV per atom. Additionally, SHapley Additive exPlanations (SHAP) analysis is employed to elucidate the fundamental principles behind the predictions by assessing the contribution of each feature to the formation energy. To validate the reliability of the models, first‐principles calculations are conducted to predict the formation energy of materials of GaP, ZnGeP 2 , and CdSiP 2 . The error range between the model predictions and the Generalized Gradient Approximation (GGA) calculated values is ≈0.1 eV per atom, confirming the accuracy of the models.
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