Inclusion of multiple cycling of potential in the deep neural network classification of voltammetric reaction mechanisms
Luke Gundry, Gareth F. Kennedy, Alan M. Bond, Jie Zhang
Abstract
are first and second order follow up chemical reactions, respectively) are demonstrated with noisy simulated data for conditions where all mechanisms are close to chemically reversible and hence difficult to distinguish, even by an experienced electrochemist. Challenges anticipated in applying the new DNN to the classification of experimental data are highlighted. Directions for future development are also discussed.
Topics & Concepts
Artificial neural networkComputer scienceDeep neural networksArtificial intelligenceMechanism (biology)Training setMachine learningPattern recognition (psychology)Biological systemChemistryPhysicsBiologyQuantum mechanicsElectrochemical Analysis and ApplicationsMachine Learning in Materials ScienceAdvanced Chemical Sensor Technologies