Abstract

A method is proposed for the neural network based analysis of the existence and stability of grain boundary complexions formed at high-symmetry tilt boundaries Σ3 (111) and Σ5 (210) in a polycrystalline Ni(Bi) solid solution. This method is based on the use of reference interparticle interaction potentials constructed within the framework of the density functional theory in combination with the structural capabilities of an artificial two-level self-learning neural network. The absolute error in determining potential energy by the neurosystem analysis is 0.012 eV/atom. The values of the formation enthalpy of grain boundary complexions for Σ3 and Σ5 boundaries are in rather good agreement with the published results of simulating this system and experimental data.

中文翻译：

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基于神经网络的简单对称倾斜边界Σ3（111）和Σ5（210）局部的晶界肤色建模

摘要

提出了一种基于神经网络的方法，用于分析多晶Ni（Bi）固溶体中高对称倾斜边界Σ3（111）和Σ5（210）处形成的晶界复合物的存在和稳定性。该方法基于在密度泛函理论框架内构建的参考粒子间相互作用势，以及人工二级自学习神经网络的结构能力。通过神经系统分析确定势能的绝对误差为0.012 eV /原子。Σ3和Σ5晶界的晶界配合物形成焓值与模拟该系统和实验数据的已公布结果非常吻合。