论文标题
玻璃体二氧化硅热力学稳定性的结构特征：机器学习和分子动力学模拟的见解
Structural Signatures for Thermodynamic Stability in Vitreous Silica: Insight from Machine Learning and Molecular Dynamics Simulations
论文作者
论文摘要
使用机器学习和由24,157个固有结构的库进行了玻璃体二氧化硅的结构 - 热动力稳定性关系，该结构由熔融和复制品交换分子动力学模拟产生。我们发现热力学稳定性，即固有结构的焓（$ e _ {\ mathrm {is}} $），可以通过数字结构描述符中的线性和非线性机器学习模型来准确预测，这些模型通常用于表征无序结构。我们发现，短距离特征在脆弱到突变的过渡以下的热力学稳定性较少。另一方面，中等范围的功能，尤其是在2.8-〜6 $ \ unicode {x212b} $;中的功能，显示了与液体和玻璃区域之间的$ e _ {\ mathrm {is}} $保持一致的相关性，并且被发现对来自不同长度尺度的功能之间的稳定性预测是最关键的。基于机器学习模型，确定了最可预测的五个结构性特征。
The structure-thermodynamic stability relationship in vitreous silica is investigated using machine learning and a library of 24,157 inherent structures generated from melt-quenching and replica exchange molecular dynamics simulations. We find the thermodynamic stability, i.e., enthalpy of the inherent structure ($e_{\mathrm{IS}}$), can be accurately predicted by both linear and nonlinear machine learning models from numeric structural descriptors commonly used to characterize disordered structures. We find short-range features become less indicative of thermodynamic stability below the fragile-to-strong transition. On the other hand, medium-range features, especially those between 2.8-~6 $\unicode{x212B}$;, show consistent correlations with $e_{\mathrm{IS}}$ across the liquid and glass regions, and are found to be the most critical to stability prediction among features from different length scales. Based on the machine learning models, a set of five structural features that are the most predictive of the silica glass stability is identified.
