论文标题
来自生成机器学习模型的数百种新的，稳定的一维材料
Hundreds of new, stable, one-dimensional materials from a generative machine learning model
论文作者
论文摘要
我们使用生成性神经网络模型来创建数千种新的一维材料。使用计算1D材料数据库（C1DB）数据库的508个稳定的一维材料对该模型进行训练。显示了500多个新材料，其密度功能理论计算是动态稳定的，并且在已知材料凸壳的0.2 eV中的形成热。在培训材料中，化学元素替代也可以获得一些新材料，但也生产了全新的材料。计算了新材料的带结构，状态的电子密度，工作函数，有效质量和声子光谱，并将数据添加到C1DB中。
We use a generative neural network model to create thousands of new, one-dimensional materials. The model is trained using 508 stable one-dimensional materials from the Computational 1D Materials Database (C1DB) database. More than 500 of the new materials are shown with density functional theory calculations to be dynamically stable and with heats of formation within 0.2 eV of the convex hull of known materials. Some of the new materials could also have been obtained by chemical element substitution in the training materials, but completely new classes of materials are also produced. The band structures, electronic densities of states, work functions, effective masses, and phonon spectra of the new materials are calculated, and the data are added to C1DB.
