结合机器学习和物理学来理解玻璃系统
Combining Machine Learning and Physics to Understand Glassy Systems
摘要：
我们对过冷液体和玻璃的理解远远落后于简单液体和结晶固体。这部分是由于许多可能相关的自由度，这些自由度是由于这些系统固有的无序性而存在的，部分是由于在统计物理学的标准背景下难以处理的非平衡效应。这些问题共同导致了一个理论不受实验约束的领域，并且基本问题仍未得到解决。平均场的结果已经成功地在无限维，但目前还不清楚在何种程度上，他们适用于现实的系统，并假设均匀的局部结构。与此相矛盾的是那些认为存在结构缺陷的理论。然而，直到最近，还不可能找到预测动态的结构特征。在这里，我们总结和重铸的结果，从最近的几篇论文提供了一个数据驱动的方法来建立一个无序材料的现象学理论相结合的机器学习与物理直觉。
英文摘要：
Our understanding of supercooled liquids and glasses has lagged significantly behind that of simple liquids and crystalline solids. This is in part due to the many possibly relevant degrees of freedom that are present due to the disorder inherent to these systems and in part to non-equilibrium effects which are difficult to treat in the standard context of statistical physics. Together these issues have resulted in a field whose theories are under-constrained by experiment and where fundamental questions are still unresolved. Mean field results have been successful in infinite dimensions but it is unclear to what extent they apply to realistic systems and assume uniform local structure. At odds with this are theories premised on the existence of structural defects. However, until recently it has been impossible to find structural signatures that are predictive of dynamics. Here we summarize and recast the results from several recent papers offering a data driven approach to building a phenomenological theory of disordered materials by combining machine learning with physical intuition.
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。
