Motevalli, BenyaminHyde, LachlanFox, Bronwyn L.Barnard, Amanda2022-12-122022-12-122513-0390http://hdl.handle.net/1885/282294Although it has been well established that the stability and properties of graphene oxide nanostructure are strongly influenced by the concentration, type, and distribution of oxygen groups on the surface, there has yet to be a definitive way of predicting the thermochemical stability in advance of detailed and time-consuming experimentation or simulation. In this study, a data set of over 60 000 unique graphene oxide nanoflakes and supervised machine learning methods are used to predict the probability of observation (stability) with perfect accuracy, based on a limited set of structural features that can be controlled in advance. A decision tree is used to show how the features determine the stability, and a neural network provides an equation to predict the thermodynamic stability of virtually any configuration in minutes. This enables researchers to use machine learning as research planning tool or to assist in analyzing results from microanalysis.Computational resources for this project were supplied by the National Computing Infrastructure (NCI) national facility under partner Grant p00. Open access publishing facilitated by Australian National University, as part of the Wiley - Australian National University agreement via the Council of Australian University Librarians.application/pdfen-AU© 2022 The Authors. Advanced Theory and Simulations published by Wiley-VCH GmbH.https://creativecommons.org/licenses/by-nc-nd/4.0/designgraphene oxidemachine learningmanufacture202210.1002/adts.202200013Creative Commons Attribution-NonCommercial-NoDerivs License