arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.09591cond-mat.mtrl-sci
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神经网络势对不同化学计量比的可传递性:Mn $_x$ Ge $_y$化合物的声子和热导率

Transferability of neural network potentials for varying stoichiometry: phonons and thermal conductivity of Mn$_x$Ge$_y$ compounds

Claudia Mangold, Shunda Chen, Giuseppe Barbalinardo, Joerg Behler, Pascal Pochet, Konstantinos Termentzidis, Yang Han, Laurent Chaput, David Lacroix, Davide Donadio

摘要:

锗锰化合物具有多种稳定相和亚稳定相,具有不同的化学计量比。这些材料在其块状形式和异质结构中都具有有趣的电子、磁性和热性质。在这里,我们开发和验证一个可转移的机器学习的潜力,基于高维神经网络的形式主义,使锰$_x$锗$_y$材料的研究在很宽的范围内的组成。我们发现,一个神经网络潜在的最小训练集拟合成功地再现了不同的局部化学环境的系统的结构和振动特性和热导率,它可以用来预测纳米异质结构中的声子效应。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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