机译:基于密度泛函理论与机器学习相结合的弹性常数预测的新方法
Chinese Acad Sci Comp Network Informat Ctr Beijing 100190 Peoples R China;
Chinese Acad Sci Comp Network Informat Ctr Beijing 100190 Peoples R China;
Chinese Acad Sci Inst Solid State Phys Hefei 230031 Anhui Peoples R China;
Chinese Acad Sci Inst Solid State Phys Hefei 230031 Anhui Peoples R China;
Chinese Acad Sci Comp Network Informat Ctr Beijing 100190 Peoples R China;
Chinese Acad Sci Comp Network Informat Ctr Beijing 100190 Peoples R China;
Prediction of elastic constants; Materials informatics; DFT calculation; Neural network; General regression neural network; Support vector regression;
机译:基于密度泛函理论与机器学习相结合的弹性常数预测的新方法
机译:红外,压电和介质响应的高通量密度函数扰动理论和机器学习预测
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机译:基于密度函数理论的石膏结构弹性常数和均质模态
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机译:机器学习用于预测由密度泛函理论获得的分子偶极矩
机译:AlN,GaN和InN的结构,能级,形成焓,弹性常数,极化和压电常数的第一性原理预测:局部校正和梯度校正的密度泛函理论