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...

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Autores principales:	Elisabeth J. Schiessler, Tim Würger, Sviatlana V. Lamaka, Robert H. Meißner, Christian J. Cyron, Mikhail L. Zheludkevich, Christian Feiler, Roland C. Aydin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/60d8828f3c82490391add887e60612fd
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spelling	oai:doaj.org-article:60d8828f3c82490391add887e60612fd2021-12-05T12:10:18ZPredicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models10.1038/s41524-021-00658-72057-3960https://doaj.org/article/60d8828f3c82490391add887e60612fd2021-12-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00658-7https://doaj.org/toc/2057-3960Abstract 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.Elisabeth J. SchiesslerTim WürgerSviatlana V. LamakaRobert H. MeißnerChristian J. CyronMikhail L. ZheludkevichChristian FeilerRoland C. AydinNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Elisabeth J. Schiessler Tim Würger Sviatlana V. Lamaka Robert H. Meißner Christian J. Cyron Mikhail L. Zheludkevich Christian Feiler Roland C. Aydin Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models
description	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.
format	article
author	Elisabeth J. Schiessler Tim Würger Sviatlana V. Lamaka Robert H. Meißner Christian J. Cyron Mikhail L. Zheludkevich Christian Feiler Roland C. Aydin
author_facet	Elisabeth J. Schiessler Tim Würger Sviatlana V. Lamaka Robert H. Meißner Christian J. Cyron Mikhail L. Zheludkevich Christian Feiler Roland C. Aydin
author_sort	Elisabeth J. Schiessler
title	Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models
title_short	Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models
title_full	Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models
title_fullStr	Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models
title_full_unstemmed	Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models
title_sort	predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/60d8828f3c82490391add887e60612fd
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Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models

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