Machine learning for the prediction of molecular dipole moments obtained by density functional theory
出版年份 2018 全文链接
标题
Machine learning for the prediction of molecular dipole moments obtained by density functional theory
作者
关键词
Density functional theory (DFT), Molecular dipole moment, Quantitative structure property relationships (QSPR), Machine learning (ML), Partial atomic charges
出版物
Journal of Cheminformatics
Volume 10, Issue 1, Pages -
出版商
Springer Nature America, Inc
发表日期
2018-08-22
DOI
10.1186/s13321-018-0296-5
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