| People | Locations | Statistics |
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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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Banko, Lars
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (26/26 displayed)
- 2024From quinary Co–Cu–Mo–Pd–Re materials libraries to gas diffusion electrodes for Alkaline hydrogen evolutioncitations
- 2023Speeding up high-throughput characterization of materials libraries by active learning: autonomous electrical resistance measurementscitations
- 2023Oxidative depolymerisation of kraft lignincitations
- 2023Microscale combinatorial libraries for the discovery of high-entropy materialscitations
- 2022Composition and electrical resistance results of a Ir-Pd-Pt-Rh-Ru composition spread thin film materials library
- 2022Zooming‐in - visualization of active site heterogeneity in high entropy alloy electrocatalysts using scanning electrochemical cell microscopycitations
- 2022Unusual phase formation in reactively sputter‐deposited La-Co-O thin‐film librariescitations
- 2022Computationally accelerated experimental materials characterizationcitations
- 2022Unravelling composition-activity-stability trends in high entropy alloy electrocatalysts by using a data‐guided combinatorial synthesis strategy and computational modelingcitations
- 2021Deep learning for visualization and novelty detection in large X-ray diffraction datasetscitations
- 2021Bayesian optimization of high‐entropy alloy compositions for electrocatalytic oxygen reductioncitations
- 2020Structure Zone Investigation of Multiple Principle Element Alloy Thin Films as Optimization for Nanoindentation Measurementscitations
- 2020Influences of Cr content on the phase transformation properties and stress change in V-Cr-O thin-film librariescitations
- 2020Fast-track to research data management in experimental material science - setting the ground for research group level materials digitalizationcitations
- 2020Predicting structure zone diagrams for thin film synthesis by generative machine learningcitations
- 2020Comparative study of the residual stress development in HMDSN-based organosilicon and silicon oxide coatingscitations
- 2020Structure zone investigation of multiple principle element alloy thin films as optimization for nanoindentation measurements
- 2020Crystallography companion agent for high-throughput materials discovery
- 2020High-throughput characterization of (Fe<sub><i>x</i></sub>Co<sub>1–<i>x</i></sub>)<sub>3</sub>O<sub>4</sub> thin-film composition spreadscitations
- 2019Mastering processing-microstructure complexity through the prediction of thin film structure zone diagrams by generative machine learning models
- 2019Mastering processing-microstructure complexity through the prediction of thin film structure zone diagrams by generative machine learning models
- 2019Effects of the Ion to growth flux ratio on the constitution and mechanical properties of Cr1–x-Alx-N thin filmscitations
- 2019Ion energy control via the electrical asymmetry effect to tune coating properties in reactive radio frequency sputteringcitations
- 2019Ion energy control via the electrical asymmetry effect to tune coating properties in reactive radio frequency sputteringcitations
- 2018Improved homogeneity of plasma and coating properties using a lance matrix gas distribution in MW-PECVDcitations
- 2018PEALD of SiO2 and Al2O3 thin films on polypropylenecitations
Places of action
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document
Mastering processing-microstructure complexity through the prediction of thin film structure zone diagrams by generative machine learning models
Abstract
Thin films are ubiquitous in modern technology and highly useful in materials discovery and design. For achieving optimal extrinsic properties their microstructure needs to be controlled in a multi-parameter space, which usually requires a too-high number of experiments to map. We propose to master thin film processing microstructure complexity and to reduce the cost of microstructure design by joining combinatorial experimentation with generative deep learning models to extract synthesis-composition-microstructure relations. A generative machine learning approach comprising a variational autoencoder and a conditional generative adversarial network predicts structure zone diagrams. We demonstrate that generative models provide a so far unseen level of quality of generated structure zone diagrams comprising chemical and processing complexity for the optimization of chemical composition and processing parameters to achieve a desired microstructure.