Fitzner Martin, Pedevilla Philipp, Michaelides Angelos
Thomas Young Centre, London Centre for Nanotechnology and Department of Physics and Astronomy, University College London, Gower Street, London, WC1E 6BT, UK.
Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
Nat Commun. 2020 Sep 22;11(1):4777. doi: 10.1038/s41467-020-18605-3.
Water in nature predominantly freezes with the help of foreign materials through a process known as heterogeneous ice nucleation. Although this effect was exploited more than seven decades ago in Vonnegut's pioneering cloud seeding experiments, it remains unclear what makes a material a good ice former. Here, we show through a machine learning analysis of nucleation simulations on a database of diverse model substrates that a set of physical descriptors for heterogeneous ice nucleation can be identified. Our results reveal that, beyond Vonnegut's connection with the lattice match to ice, three new microscopic factors help to predict the ice nucleating ability. These are: local ordering induced in liquid water, density reduction of liquid water near the surface and corrugation of the adsorption energy landscape felt by water. With this we take a step towards quantitative understanding of heterogeneous ice nucleation and the in silico design of materials to control ice formation.
自然界中的水主要借助外来物质通过一种称为异质冰核形成的过程结冰。尽管这种效应在七十多年前冯内古特开创性的云催化实验中就已被利用，但目前仍不清楚是什么使得一种物质成为良好的冰核形成剂。在此，我们通过对各种模型底物数据库上的成核模拟进行机器学习分析表明，可以识别出一组用于异质冰核形成的物理描述符。我们的结果表明，除了冯内古特所提出的与冰的晶格匹配之外，还有三个新的微观因素有助于预测冰核形成能力。它们是：液态水中诱导的局部有序性、表面附近液态水的密度降低以及水所感受到的吸附能景观的起伏。借此，我们朝着定量理解异质冰核形成以及通过计算机模拟设计控制冰形成的材料迈出了一步。
