| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Feiler, Christian
Helmholtz-Zentrum Hereon
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (8/8 displayed)
- 2024Exploring the Effect of Microstructure and Surface Recombination on Hydrogen Effusion in Zn–Ni‐Coated Martensitic Steels by Advanced Computational Modelingcitations
- 2023Searching the chemical space for effective magnesium dissolution modulators: a deep learning approach using sparse features
- 2023Predicting corrosion inhibition efficiencies of small organic molecules using data-driven techniques
- 2021Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models
- 2021Exploring the Structure-Property Relationship of Magnesium Dissolution Modulatorscitations
- 2020A first-principles analysis of the charge transfer in magnesium corrosioncitations
- 2019Data science based mg corrosion engineeringcitations
- 2019Data science based mg corrosion engineering
Places of action
| Organizations | Location | People |
|---|
article
Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models
Abstract
The degradation behaviour of magnesium and its alloys can be tuned by small organic molecules. However, an automatic identification of effective organic additives within the vast chemical space of potential compounds needs sophisticated tools. Herein, we propose two systematic approaches of sparse feature selection for identifying molecular descriptors that are most relevant for the corrosion inhibition efficiency of chemical compounds. One is based on the classical statistical tool of analysis of variance, the other one based on random forests. We demonstrate how both can—when combined with deep neural networks—help to predict the corrosion inhibition efficiencies of chemical compounds for the magnesium alloy ZE41. In particular, we demonstrate that this framework outperforms predictions relying on a random selection of molecular descriptors. Finally, we point out how autoencoders could be used in the future to enable even more accurate automated predictions of corrosion inhibition efficiencies.