机译:密度泛函理论计算与机器学习技术相结合的无机化合物带隙预测模型
Department of Materials Science and Engineering, Kyoto University, Kyoto 606-8501, Japan;
Department of Materials Science and Engineering, Kyoto University, Kyoto 606-8501, Japan,Elements Strategy Initiative for Structure Materials (ESISM), Kyoto University, Kyoto 606-8501, Japan,Center for Materials Research by Information Integration, National Institute for Materials Science (NIMS), Tsukuba 305-0047, Japan;
Department of Materials Science and Engineering, Kyoto University, Kyoto 606-8501, Japan,Nanostructures Research Laboratory, Japan Fine Ceramics Center, Nagoya 456-8587, Japan;
Department of Materials Science and Engineering, Kyoto University, Kyoto 606-8501, Japan;
Department of Materials Science and Engineering, Kyoto University, Kyoto 606-8501, Japan,Elements Strategy Initiative for Structure Materials (ESISM), Kyoto University, Kyoto 606-8501, Japan,Center for Materials Research by Information Integration, National Institute for Materials Science (NIMS), Tsukuba 305-0047, Japan,Nanostructures Research Laboratory, Japan Fine Ceramics Center, Nagoya 456-8587, Japan;
机译:密度泛函理论计算与机器学习技术相结合的无机化合物带隙预测模型
机译:Li(Ni,Co,Al)O-2阴极性司司系:拓扑分析,密度函数理论,中子衍射和机器学习技术的组合
机译:机器学习辅助官能化MXENE的准确频带差距预测
机译:基于密度的功能理论(DFT)的GW + BSE计算,以研究Ga与Al在带隙的影响和Al_xga_(1-x)n的光谱的影响(x = 0,0.125,0.25,1)
机译:基于密度泛函理论和机器学习技术的抑酸增稠剂技术研究
机译:使用Wannier函数改善密度泛函理论中的固体带隙预测
机译:aX二元化合物带隙预测模型的组合 密度泛函理论计算和机器学习技术