SCMs文章合辑|机器学习与材料计算
本文精选了Science China Materials近期发表的机器学习与材料计算领域的代表性成果，介绍给读者。敬请关注！
Transferable prediction of intermolecular coupling achieved by hierarchical material representation
创建层次化材料表象以实现分子间耦合的可迁移预测
Chong Li, Chao Liang, Yilimiranmu Rouzhahong, Biao Wang, Huashan Li
Science China Materials, 66(2), 819-826 (2023)
https://doi.org/10.1007/s40843-022-2198-5
A universal model for accurately predicting the formation energy of inorganic compounds
一种预测无机晶体形成能的高精度泛化模型
Yingzong Liang, Mingwei Chen, Yanan Wang, Huaxian Jia, Tenglong Lu, Fankai Xie, Guanghui Cai, Zongguo Wang, Sheng Meng, Miao Liu
Science China Materials, 66(1), 343-351 (2023)
https://doi.org/10.1007/s40843-022-2134-3
Graph representation-based machine learning framework for predicting electronic band structures of quantum-confined nanostructures
基于图表示的机器学习框架预测量子受限体系的电子能带结构
Zifeng Wang, Shizhuo Ye, Hao Wang, Qijun Huang, Jin He, Sheng Chang
Science China Materials, 65(11), 3157-3170 (2022)
https://doi.org/10.1007/s40843-022-2103-9
机器学习原子运动揭示金属玻璃塑性起源：从热塑性到超声塑性
Xiaodi Liu, Quanfeng He, Wenfei Lu, Ziqing Zhou, Jinsen Tian, Dandan Liang, Jiang Ma, Yong Yang, Jun Shen
Science China Materials, 65(7), 1952-1962 (2022)
https://doi.org/10.1007/s40843-021-1990-2
一种通用的块体热电材料高通量实验筛选策略
Shiyang He, Yang Yang, Zhili Li, Jiye Zhang, Chenyang Wang, Wenqing Zhang, Jun Luo
Science China Materials, 64(7), 1751-1760 (2021)
https://doi.org/10.1007/s40843-020-1568-5
机器学习方法识别金属玻璃中不同动力学原子的结构特征
Yicheng Wu, Wei-Hua Wang, Pengfei Guan, Haiyang Bai
Science China Materials, 64(7), 1820-1826 (2021)
https://doi.org/10.1007/s40843-020-1626-3
First-principles study of the anisotropic thermal expansion and thermal transport properties in h-BN
h-BN各向异性热膨胀和热输运特性的第一性原理研究
Bo Niu, Lixiang Zhong, Wei Hao, Zhihua Yang, Xiaoming Duan, Delong Cai, Peigang He, Dechang Jia, Shuzhou Li, Yu Zhou
Science China Materials, 64(4), 953-963 (2021)
https://doi.org/10.1007/s40843-020-1527-0
二维过渡金属三元硫属化合物中实现量子反常霍尔效应的理论设计
Wenjia Yang, Yaling Zhang, Jingjing Zhang, Huisheng Zhang, Xiaohong Xu
Science China Materials, 66(3), 1165-1171 (2023)
https://doi.org/10.1007/s40843-022-2248-2
单层半导体性γ相第四主族单硫化合物中的拓扑缺陷及其诱导的金属性
Shengfeng Zeng, Xiaolong Zou
Science China Materials, 66(3), 1132-1139 (2023)
https://doi.org/10.1007/s40843-022-2221-y
2D indium-VA semiconductors: Promising photocatalysts with intrinsic electric fields for water splitting
2D铟-VA半导体：具有本征电场的水裂解光催化剂
Faling Ling, Xiaoqing Liu
https://doi.org/10.1007/s40843-022-2432-3
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