文章基本信息

标题：Accurate prediction of ice nucleation from room temperature water
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作者：Michael Benedict Davies ; Martin Fitzner ; Angelos Michaelides 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2022
卷号：119
期号：31
DOI：10.1073/pnas.2205347119
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Significance From glaciers, to cryopreservation, to climate modeling, the ability of materials to promote ice nucleation is at the heart of a myriad of technologies and natural phenomena. Predicting the ice nucleation ability of materials thus presents great opportunity; however, it has presented an equally great challenge: despite over 75 y of research, no reliable method or guideline exists. Here we address this by developing a model that accurately predicts the ice nucleation ability of materials. Deep learning techniques enable an easy, cheap, and rapid method that requires just an image of room temperature water in contact with a substrate as input. Remarkably, the model shows that the interfacial structure of water alone is sufficient to determine nucleation. Crystal nucleation is one of the most fundamental processes in the physical sciences and almost always occurs heterogeneously with the aid of a nucleating substrate. No example of nucleation is more ubiquitous and impactful than the formation of ice, vital to fields as diverse as geology, biology, aeronautics, and climate science. However, despite considerable effort, we still cannot predict a priori the efficacy of a nucleating agent. Here we utilize deep learning methods to accurately predict nucleation ability from images of room temperature liquid water—generated from molecular dynamics simulations—on a broad range of substrates. The resulting model, named IcePic, can rapidly and accurately infer nucleation ability, eliminating the requirement for either notoriously expensive simulations or direct experimental measurement. In an online poll, IcePic was found to significantly outperform humans in predicting the ice nucleating efficacy of materials. By analyzing the typical errors made by humans, as well as the application of reverse interpretation methods, physical insights into the role the water contact layer plays in ice nucleation have been obtained. Moving forward, we suggest that IcePic can be used as an easy, cheap, and rapid way to discern the nucleation ability of substrates, also with potential for learning other properties related to interfacial water.
关键词：enicenucleationdeep learning