Predicting the Probability of Observation of Arbitrary Graphene Oxide Nanoflakes Using Artificial Neural Networks (Adv. Theory Simul. 5/2022). Issue 5 (12th May 2022) - Motevalli, Benyamin; Hyde, Lachlan; Fox, Bronwyn L.; Barnard, Amanda S.
Abstract : Predicting the Probability of Observation of Arbitrary Graphene Oxide Nanoflakes Using Artificial Neural Networks Predicting the stability of graphene oxide nanostructures is a requirement for ISO standards, but is technically challenging. In article 202200013, Motevalli and coworkers train a neural network to predict the stability based on a limited set of experimentally accessible characteristics, resulting in an analytical expression for calculating the stability of hypothetical structures, helping to anticipate their most probable properties.