玻璃体二氧化硅热力学稳定性的结构特征:来自机器学习和分子动力学模拟的见解

Zheng Yu, Qitong Liu, I. Szlufarska, Bu Wang
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引用次数: 4

摘要

利用机器学习和由熔体淬火和复制交换分子动力学模拟生成的24,157个固有结构库,研究了玻璃体二氧化硅的结构-热力学稳定性关系。我们发现热力学稳定性,即固有结构($e_{\mathrm{IS}}$)的焓,可以通过通常用于表征无序结构的数字结构描述符的线性和非线性机器学习模型准确预测。我们发现,在从脆弱到强大的转变过程中,短期特征越来越不能表明热力学稳定性。而中量程特征,特别是2.8 ~6 $\unicode{x212B}$;与$e_{\mathrm{IS}}$在液相区和玻璃区表现出一致的相关性,对不同长度尺度特征的稳定性预测最为关键。基于机器学习模型,确定了最能预测二氧化硅玻璃稳定性的五种结构特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structural signatures for thermodynamic stability in vitreous silica: Insight from machine learning and molecular dynamics simulations
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.
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