Predicting structure zone diagrams for thin film synthesis by generative machine learning
| Autor: | Yury Lysogorskiy, Alfred Ludwig, Lars Banko, Dennis Naujoks, Dario Grochla, Ralf Drautz |
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| Rok vydání: | 2020 |
| Předmět: |
010302 applied physics
Structure (mathematical logic) Computer science business.industry Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology 021001 nanoscience & nanotechnology Machine learning computer.software_genre Microstructure 01 natural sciences Mechanics of Materials 0103 physical sciences TA401-492 General Materials Science Artificial intelligence Thin film 0210 nano-technology business Materials of engineering and construction. Mechanics of materials Generative adversarial network computer Generative grammar |
| Zdroj: | Communications Materials, Vol 1, Iss 1, Pp 1-10 (2020) |
| ISSN: | 2662-4443 |
| Popis: | 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 too high a number of experiments to map. Here, 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 using 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 that can be applied for the optimization of chemical composition and processing parameters to achieve a desired microstructure. Controlling the microstructure of thin films is vital for tuning their properties. Here, machine learning is applied to obtain synthesis-composition-microstructure relationships in the form of structure zone diagrams for thin films, enabling microstructure prediction. |
| Databáze: | OpenAIRE |
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