Tiago L. P. Galvão, Inês Ferreira, Frederico Maia, José R. B. Gomes, João Tedim
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引用次数: 0
Abstract
The machine learning framework reported herein can greatly accelerate the development of more effective and sustainable corrosion inhibitors for aluminum alloys, which still rely mostly on the experience of corrosion scientists, and trial and error laboratory testing. It can be used to design inhibitors for specific applications, which can be immobilized into nanocontainers or included directly into coatings in the search for less hazardous corrosion protective technologies. Therefore, a machine learning (ML) classification model that allows to identify promising compounds ( > 70% inhibitor efficiency) among less promising ones, and an online application ( https://datacor.shinyapps.io/datacortech/ ) were developed for the virtual screen (simulation) of potential inhibitors for aluminum alloys, capable of considering the molecular structure and the influence of pH as an input.
期刊介绍:
npj Materials Degradation considers basic and applied research that explores all aspects of the degradation of metallic and non-metallic materials. The journal broadly defines ‘materials degradation’ as a reduction in the ability of a material to perform its task in-service as a result of environmental exposure.
The journal covers a broad range of topics including but not limited to:
-Degradation of metals, glasses, minerals, polymers, ceramics, cements and composites in natural and engineered environments, as a result of various stimuli
-Computational and experimental studies of degradation mechanisms and kinetics
-Characterization of degradation by traditional and emerging techniques
-New approaches and technologies for enhancing resistance to degradation
-Inspection and monitoring techniques for materials in-service, such as sensing technologies