基于原子分子动力学模拟和机器学习算法的聚酰亚胺玻璃态Transition Temperature的测定
Determination of Glass Transition Temperature of Polyimides from Atomistic Molecular Dynamics Simulations and Machine-Learning Algorithms
摘要：
玻璃化转变温度（$T_{\text{g}}$）在控制聚合物的机械和热性能方面起重要作用。聚酰亚胺是一类重要的聚合物，由于其优异的耐热性和机械强度而具有广泛的应用。因此，高度期望预测聚酰亚胺$a~priori$的$T_{\text{g}}$的能力，以加快具有目标性质和应用的新聚酰亚胺聚合物的设计和发现。在这里，我们探索了三种不同的方法来计算$T_{\text{g}}$的聚酰亚胺通过全原子分子动力学（MD）模拟或预测$T_{\text{g}}$通过使用机器学习算法分析现有的数据从文献中生成的数学模型。我们的模拟表明，可以通过检查简单气体分子在聚酰亚胺中的扩散系数作为温度的函数来确定$T_{\text{g}}$，并且结果与从聚合物密度与温度的数据中得出的结果相当，并且实际上更接近可用的实验数据。此外，用机器学习算法导出的$T_{\text{g}}$的预测模型可用于在约20度的不确定度内成功地估计$T_{\text{g}}$，即使对于尚未实验合成的聚酰亚胺也是如此。
英文摘要：
Glass transition temperature ($T_{\text{g}}$) plays an important role in controlling the mechanical and thermal properties of a polymer. Polyimides are an important category of polymers with wide applications because of their superior heat resistance and mechanical strength. The capability of predicting $T_{\text{g}}$ for a polyimide $a~priori$ is therefore highly desirable in order to expedite the design and discovery of new polyimide polymers with targeted properties and applications. Here we explore three different approaches to either compute $T_{\text{g}}$ for a polyimide via all-atom molecular dynamics (MD) simulations or predict $T_{\text{g}}$ via a mathematical model generated by using machine-learning algorithms to analyze existing data collected from literature. Our simulations reveal that $T_{\text{g}}$ can be determined from examining the diffusion coefficient of simple gas molecules in a polyimide as a function of temperature and the results are comparable to those derived from data on polymer density versus temperature and actually closer to the available experimental data. Furthermore, the predictive model of $T_{\text{g}}$ derived with machine-learning algorithms can be used to estimate $T_{\text{g}}$ successfully within an uncertainty of about 20 degrees, even for polyimides yet to be synthesized experimentally.
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。
