Issue 12, 2021

Predicting hydration layers on surfaces using deep learning

Abstract

Characterisation of the nanoscale interface formed between minerals and water is essential to the understanding of natural processes, such as biomineralization, and to develop new technologies where function is dominated by the mineral–water interface. Atomic force microscopy offers the potential to characterize solid–liquid interfaces in high-resolution, with several experimental and theoretical studies offering molecular scale resolution by linking measurements directly to water density on the surface. However, the theoretical techniques used to interpret such results are computationally intensive and development of the approach has been limited by interpretation challenges. In this work, we develop a deep learning architecture to learn the solid–liquid interface of polymorphs of calcium carbonate, allowing for the rapid predictions of density profiles with reasonable accuracy.

Graphical abstract: Predicting hydration layers on surfaces using deep learning

Article information

Article type
Communication
Submitted
03 Apr 2021
Accepted
03 May 2021
First published
06 May 2021
This article is Open Access
Creative Commons BY license

Nanoscale Adv., 2021,3, 3447-3453

Predicting hydration layers on surfaces using deep learning

Y. S. Ranawat, Y. M. Jaques and A. S. Foster, Nanoscale Adv., 2021, 3, 3447 DOI: 10.1039/D1NA00253H

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