论文标题
通过机器学习在眼镜中找到缺陷
Finding defects in glasses through machine learning
论文作者
论文摘要
结构缺陷控制眼镜的动力学，热力学和机械性能。例如，稀有的量子隧道两级系统（TLS）在非常低温下控制眼镜的物理。由于它们的密度极低，因此很难在计算机模拟中直接识别它们。我们引入了一种机器学习方法，以有效地探索玻璃模型的势能格局并确定所需的缺陷类别。我们特别关注TLS，我们设计了一种算法，该算法能够快速预测经典模拟产生的任何两种非晶构型之间的量子分裂。反过来，这使我们能够将计算努力转移到收集和识别大量TLS的情况下，而不是对非隧道缺陷的无用表征，这些缺陷更加丰富。最后，我们解释了我们的机器学习模型，以了解如何识别和表征TLS，从而直接对其微观性质进行物理见解。
Structural defects control the kinetic, thermodynamic and mechanical properties of glasses. For instance, rare quantum tunneling two-level systems (TLS) govern the physics of glasses at very low temperature. Because of their extremely low density, it is very hard to directly identify them in computer simulations. We introduce a machine learning approach to efficiently explore the potential energy landscape of glass models and identify desired classes of defects. We focus in particular on TLS and we design an algorithm that is able to rapidly predict the quantum splitting between any two amorphous configurations produced by classical simulations. This in turn allows us to shift the computational effort towards the collection and identification of a larger number of TLS, rather than the useless characterization of non-tunneling defects which are much more abundant. Finally, we interpret our machine learning model to understand how TLS are identified and characterized, thus giving direct physical insight into their microscopic nature.
