Tiago L. P. Galvgk1, Jose R. B. Gomes： Joao Tedim2
1 CICECO-Aveiro Institute of Materials, Department of Materials and Ceramic Engineering,
University of Aveiro, 3810-193 Aveiro, Portugal;
2CICECO-Aveiro Institute of Materials, Department of Chemistry,
University of Aveiro, 3810-193 Aveiro, Portugal
EXTENDED ABSTRACT: The CORD ATA (https://datacor.shinyapps.io/cordata/) and DATACORTECH (https ://datacor. shinyapps.io/datacortech/) cloud applications intend to digitalize and accelerate the search for corrosion inhibitors, thus supporting the development of more robust and condition specific corrosion-protective technologies.
The large and growing amount of corrosion inhibition efficiencies in literature requires an efficient way to organize, access and compare this data. Therefore, an open data management web application (CORDATA [1]) was developed to select appropriate corrosion inhibitors for applications with specific requirements. Nearly five thousand corrosion inhibition efficiencies and four hundred compounds have already been added to the database, fbr aluminum, copper, magnesium, and iron, together with their main alloys.The availability of larger datasets is ideal fbr the application of machine learning (ML). Data from different authors and laboratories, measured under different conditions, was used to develop a ML composite model (several conditions within the same model, as opposed to individual models for specific conditions) [2], which has served as the basis of DATACORTECH, an artificial intelligence application that is being developed fbr the virtual screen of potential corrosion inhibitors fbr the protection of aluminum alloys under different conditions.Acknowledgements: FCT project DATACOR (POCI-01 -0145-FEDER-030256 and PTDC/QUI-QFI/30256/2017, https://datacorproject.wixsite.com/ datacor) and H2020-EU MSCA-RISE-2020 COAT4LIFE (GA ID 101007430).
Keywords: Corrosion Inhibitors; Cloud Applications; Data Management; Machine Learning; Virtual Screen REFERENCES
[1] T. L. P. Galvao et al., npj Materials Degradation, 6, (2022) 48
⑵ T.L. P. Galvao et al., J. Phys. Chem. C, 124(10), (2020) 5624-5635
Tiago L. P. Galvao obtained his PhD (2013) in Chemistry from the University of Porto, Portugal, and is currently a researcher in CICECO - Aveiro Institute of Materials from the University of Aveiro, Portugal. His main interests are database production, machine learning and development of cloud applications fbr corrosion protection, design of smart nanocontainers, and nanosafety (https://orcid.org/0000-0002-0685-3675).