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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Würger, Tim
Helmholtz-Zentrum Hereon
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (10/10 displayed)
- 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
- 2020A first-principles analysis of the charge transfer in magnesium corrosioncitations
- 2020A first-principles analysis of the charge transfer in magnesium corrosioncitations
- 2020A first-principles analysis of the charge transfer in magnesium corrosioncitations
- 2020ATR-FTIR in Kretschmann configuration integrated with electrochemical cell as in situ interfacial sensitive tool to study corrosion inhibitors for magnesium substrates
- 2019Data science based mg corrosion engineeringcitations
- 2019Data science based mg corrosion engineering
- 2018Adsorption of acetone on rutile TiO2: a DFT and FTIRS study
Places of action
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article
Searching the chemical space for effective magnesium dissolution modulators: a deep learning approach using sparse features
Abstract
Small organic molecules can alter the degradation rates of the magnesium alloy ZE41. However, identifying suitable candidate compounds from the vast chemical space requires sophisticated tools. The information contained in only a few molecular descriptors derived from recursive feature elimination was previously shown to hold the potential for determining such candidates using deep neural networks. We evaluate the capability of these networks to generalise by blind testing them on 15 randomly selected, completely unseen compounds. We find that their generalisation ability is still somewhat limited, most likely due to the relatively small amount of available training data. However, we demonstrate that our approach is scalable; meaning deficiencies caused by data limitations can presumably be overcome as the data availability increases. Finally, we illustrate the influence and importance of well-chosen descriptors towards the predictive power of deep neural networks.