Machine learning for glass science and engineering: A review
| Autor: | Xinyi Xu, Han Liu, Zipeng Fu, Kai Yang, Mathieu Bauchy |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
010302 applied physics
Computer science business.industry Science and engineering Bayesian optimization 02 engineering and technology Advanced materials 021001 nanoscience & nanotechnology Machine learning computer.software_genre Condensed Matter Physics 01 natural sciences Electronic Optical and Magnetic Materials lcsh:Chemistry lcsh:QD1-999 0103 physical sciences Materials Chemistry Ceramics and Composites lcsh:TA401-492 lcsh:Materials of engineering and construction. Mechanics of materials Artificial intelligence 0210 nano-technology business computer |
| Zdroj: | Journal of Non-Crystalline Solids: X, Vol 4, Iss, Pp-(2019) |
| ISSN: | 2590-1591 |
| Popis: | The design of new glasses is often plagued by poorly efficient Edisonian “trial-and-error” discovery approaches. As an alternative route, the Materials Genome Initiative has largely popularized new approaches relying on artificial intelligence and machine learning for accelerating the discovery and optimization of novel, advanced materials. Here, we review some recent progress in adopting machine learning to accelerate the design of new glasses with tailored properties. Keywords: Composition-property relationship, Structural signature, Molecular dynamics simulation, Artificial neuron network, Bayesian optimization |
| Databáze: | OpenAIRE |
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