Predictive machine learning models trained on experimental datasets for electrochemical nitrogen reduction
Darik A. Rosser, Brianna R. Farris, Kevin C. Leonard
Abstract
Obtaining useful insights from machine learning models trained on experimental datasets collected across different groups to improve the sustainability of chemical processes can be challenging due to the small size and heterogeneity of the dataset.
Topics & Concepts
Machine learningComputer scienceArtificial intelligenceReduction (mathematics)Predictive modellingSustainabilityMathematicsBiologyGeometryEcologyAmmonia Synthesis and Nitrogen ReductionMachine Learning in Materials ScienceElectrocatalysts for Energy Conversion