论文标题
神经网络潜在的变化化学计量计：$ _x $ ge $ _y $ $化合物的声子和导热率
Transferability of neural network potentials for varying stoichiometry: phonons and thermal conductivity of Mn$_x$Ge$_y$ compounds
论文作者
论文摘要
锗锰化合物表现出各种具有不同化学计量法的稳定和亚稳态的相。这些材料都具有有趣的电子，磁性和热特性，既有其散装形式又是异质结构。在这里，我们根据高维神经网络形式主义开发并验证可转移的机器学习潜力，以使Mn $ _x $ ge $ _y $ $ y $材料在各种构图上进行研究。我们表明，在最小训练集上安装的神经网络电位成功地再现了具有不同局部化学环境的系统的结构和振动特性以及系统的导热率，并且可以用于预测纳米级异质结构中的语音效应。
Germanium manganese compounds exhibit a variety of stable and metastable phases with different stoichiometry. These materials entail interesting electronic, magnetic and thermal properties both in their bulk form and as heterostructures. Here we develop and validate a transferable machine learning potential, based on the high-dimensional neural network formalism, to enable the study of Mn$_x$Ge$_y$ materials over a wide range of compositions. We show that a neural network potential fitted on a minimal training set reproduces successfully the structural and vibrational properties and the thermal conductivity of systems with different local chemical environments, and it can be used to predict phononic effects in nanoscale heterostructures.
