| Full text | |
| Author(s): |
Sigaki, Higor Y. D.
[1]
;
Lenzi, Ervin K.
[2]
;
Zola, Rafael S.
[1, 3]
;
Perc, Matjaz
[4, 5, 6]
;
Ribeiro, V, Haroldo
Total Authors: 5
|
| Affiliation: | [1] V, Univ Estadual Maringa, Dept Fis, BR-87020900 Maringa, Parana - Brazil
[2] Univ Estadual Ponta Grossa, Dept Fis, BR-84030900 Ponta Grossa, Parana - Brazil
[3] Univ Tecnol Fed Parana, Dept Fis, BR-86812460 Apucarana, PR - Brazil
[4] Univ Maribor, Fac Nat Sci & Math, Koroska Cesta 160, Maribor 2000 - Slovenia
[5] China Med Univ, China Med Univ Hosp, Dept Med Res, Taichung - Taiwan
[6] Complex Sci Hub Vienna, Josefstadterstr 39, A-1080 Vienna - Austria
Total Affiliations: 6
|
| Document type: | Journal article |
| Source: | SCIENTIFIC REPORTS; v. 10, n. 1 MAY 6 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
Machine learning algorithms have been available since the 1990s, but it is much more recently that they have come into use also in the physical sciences. While these algorithms have already proven to be useful in uncovering new properties of materials and in simplifying experimental protocols, their usage in liquid crystals research is still limited. This is surprising because optical imaging techniques are often applied in this line of research, and it is precisely with images that machine learning algorithms have achieved major breakthroughs in recent years. Here we use convolutional neural networks to probe several properties of liquid crystals directly from their optical images and without using manual feature engineering. By optimizing simple architectures, we find that convolutional neural networks can predict physical properties of liquid crystals with exceptional accuracy. We show that these deep neural networks identify liquid crystal phases and predict the order parameter of simulated nematic liquid crystals almost perfectly. We also show that convolutional neural networks identify the pitch length of simulated samples of cholesteric liquid crystals and the sample temperature of an experimental liquid crystal with very high precision. (AU) | |
| FAPESP's process: | 14/50983-3 - INCT 2014: complex fluids |
| Grantee: | Antonio Martins Figueiredo Neto |
| Support Opportunities: | Research Projects - Thematic Grants |