Predicting the Probability of Observation of Arbitrary Graphene Oxide Nanoflakes Using Artificial Neural Networks (Adv. Theory Simul. 5/2022). Issue 5 (12th May 2022)

• Record Type: Journal Article
• Authors: Motevalli, Benyamin; Hyde, Lachlan; Fox, Bronwyn L.; Barnard, Amanda S.
• Is Part Of: Advanced theory and simulations. Volume 5:Issue 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-12
• Subjects: Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.202270013
• Languages: English
• ISSNs: 2513-0390

Abstract

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.