Generalised deep-learning workflow for the prediction of hydration layers over surfaces

YS Ranawat and YM Jaques and AS Foster, JOURNAL OF MOLECULAR LIQUIDS, 367, 120571 (2022).

DOI: 10.1016/j.molliq.2022.120571

Atomic force microscopy (AFM) is paving the way for understanding the solid-liquid interfaces at the nanoscale. These AFM studies are complemented with molecular dynamics (MD) simulations of hydra-tion layers over candidate surfaces for a comprehensive characterisation. We earlier proposed, in Ranawat et.al. (2021), a deep-learning (DL) network to predict hydration layers over the candidate sur-faces much more rapidly than computationally-intensive MD. However, the proposed elements-as -channels based network is bound to the elements present in the training surfaces. Here, we develop a generalised descriptor of the surface to train element-agnostic networks. We demonstrate the descrip- tor's efficacy by predicting the hydration layers over a dolomite surface using a network trained on the calcite and magnesite surfaces. We also demonstrate the transfer-learning capability of such a descriptor by incorporating mica into the training surfaces, and predict the pyrophyllite and boehmite surfaces. Further, we propose an energy- based DL framework to gauge the possible prediction accuracy of a network on surfaces hitherto unseen. We combine these advance techniques into a generalised work-flow to complement AFM studies. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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