Suppr超能文献

准确预测一大组有机化合物的生成焓。

Accurate prediction of enthalpies of formation for a large set of organic compounds.

机构信息

College of Chemistry, Sichuan University, Chengdu 610065, People's Republic of China.

出版信息

J Comput Chem. 2010 Nov 15;31(14):2585-92. doi: 10.1002/jcc.21550.

Abstract

This article describes a multiparameter calibration model, which improves the accuracy of density functional theory (DFT) for the prediction of standard enthalpies of formation for a large set of organic compounds. The model applies atom based, bond based, electronic, and radical environmental correction terms to calibrate the calculated enthalpies of formation at B3LYP/6-31G(d,p) level by a least-square method. A diverse data set of 771 closed-shell compounds and radicals is used to train the model. The leave-one-out cross validation squared correlation coefficient q(2) of 0.84 and squared correlation coefficient r(2) of 0.86 for the final model are obtained. The mean absolute error in enthalpies of formation for the dataset is reduced from 4.9 kcal/mol before calibration to 2.1 kcal/mol after calibration. Five-fold cross validation is also used to estimate the performance of the calibration model and similar results are obtained.

摘要

本文描述了一种多参数校准模型,该模型提高了密度泛函理论(DFT)对大量有机化合物标准生成焓预测的准确性。该模型通过最小二乘法应用基于原子、基于键、基于电子和基于自由基环境的校正项,对 B3LYP/6-31G(d,p)水平计算的生成焓进行校准。使用了 771 种闭壳化合物和自由基的多样化数据集来训练模型。最终模型的留一交叉验证平方相关系数 q(2)为 0.84,平方相关系数 r(2)为 0.86。数据集的生成焓平均绝对误差从校准前的 4.9 kcal/mol 降低到校准后的 2.1 kcal/mol。还使用五倍交叉验证来估计校准模型的性能,得到了类似的结果。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验