A Smart Forecasting System of Computational Cost of DFT calculations using Machine Learning and Chem

来源 :2019中国化学会第十五届全国计算(机)化学学术会议 | 被引量 : 0次 | 上传用户：ajimide001

【摘要】

：

【作者】

：

Shuo Ma Yingjin Ma Baohua Zhang Yingqi Tian Zhong Jin

【机构】

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Department of High Performance Computing Technology and Application Development,Computer Network Inf

【出处】

：

2019中国化学会第十五届全国计算(机)化学学术会议

【发表日期】

：

2019年3期

【关键词】

：

density-functional theory machine learning neural networks cheminformatics

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　　The Density-functional theory(DFT) should be the most popular approach that used in computational physics or computational chemistry community.Normally,the computational time is unknown until the calculation is done.And as such,researchers can hardly arrange their schedules rationally.

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A Smart Forecasting System of Computational Cost of DFT calculations using Machine Learning and Chem

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