Machine Learning Analysis of Umbilic Defect Annihilation in Nematic Liquid Crystals in the Presence of Nanoparticles
Machine learning-based image recognition is employed to investigate the annihilation dynamics of umbilic defects induced in systems of nematic liquid crystals doped with nanoparticles. A machine learning methodology based on a YOLO algorithm is trained and optimized to identify defects of strength s...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Crystals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4352/15/3/214 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849341620301856768 |
|---|---|
| author | Ingo Dierking Adam Moyle Gabriele Maria Cepparulo Katherine Skingle Laura Hernández Juhan Raidal |
| author_facet | Ingo Dierking Adam Moyle Gabriele Maria Cepparulo Katherine Skingle Laura Hernández Juhan Raidal |
| author_sort | Ingo Dierking |
| collection | DOAJ |
| description | Machine learning-based image recognition is employed to investigate the annihilation dynamics of umbilic defects induced in systems of nematic liquid crystals doped with nanoparticles. A machine learning methodology based on a YOLO algorithm is trained and optimized to identify defects of strength s = ±1 and determine their trajectories during the annihilation process of umbilics of opposite sign. Universal scaling laws describing the distance between two defects as a function of time to annihilation are determined, and average scaling exponents α are calculated for an ensemble of events. It is observed that the defect annihilation scaling exponents deviate from the theoretically predicted value of α = 1/2 when nanoparticles of varying size and concentration are introduced to the system. Scaling laws of the form D~t<sup>α</sup> do not yield the typical square-root law normally observed, but the experiments suggest a decrease in the exponent to saturation values of approximately α = 0.38 ± 0.01 as the size, particle concentration, and mass concentration of the nanoparticles is increased. Interestingly, the defect density itself is not affected, which implies that the nanoparticles do not act as defect formation sites. |
| format | Article |
| id | doaj-art-7bf7002bbd7e4f93be1b40a434ee900a |
| institution | Kabale University |
| issn | 2073-4352 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Crystals |
| spelling | doaj-art-7bf7002bbd7e4f93be1b40a434ee900a2025-08-20T03:43:36ZengMDPI AGCrystals2073-43522025-02-0115321410.3390/cryst15030214Machine Learning Analysis of Umbilic Defect Annihilation in Nematic Liquid Crystals in the Presence of NanoparticlesIngo Dierking0Adam Moyle1Gabriele Maria Cepparulo2Katherine Skingle3Laura Hernández4Juhan Raidal5Department of Physics and Astronomy, University of Manchester, Oxford Road, Manchester M139PL, UKDepartment of Physics and Astronomy, University of Manchester, Oxford Road, Manchester M139PL, UKDepartment of Physics and Astronomy, University of Manchester, Oxford Road, Manchester M139PL, UKDepartment of Physics and Astronomy, University of Manchester, Oxford Road, Manchester M139PL, UKDepartment of Physics and Astronomy, University of Manchester, Oxford Road, Manchester M139PL, UKDepartment of Physics and Astronomy, University of Manchester, Oxford Road, Manchester M139PL, UKMachine learning-based image recognition is employed to investigate the annihilation dynamics of umbilic defects induced in systems of nematic liquid crystals doped with nanoparticles. A machine learning methodology based on a YOLO algorithm is trained and optimized to identify defects of strength s = ±1 and determine their trajectories during the annihilation process of umbilics of opposite sign. Universal scaling laws describing the distance between two defects as a function of time to annihilation are determined, and average scaling exponents α are calculated for an ensemble of events. It is observed that the defect annihilation scaling exponents deviate from the theoretically predicted value of α = 1/2 when nanoparticles of varying size and concentration are introduced to the system. Scaling laws of the form D~t<sup>α</sup> do not yield the typical square-root law normally observed, but the experiments suggest a decrease in the exponent to saturation values of approximately α = 0.38 ± 0.01 as the size, particle concentration, and mass concentration of the nanoparticles is increased. Interestingly, the defect density itself is not affected, which implies that the nanoparticles do not act as defect formation sites.https://www.mdpi.com/2073-4352/15/3/214liquid crystalnematicdefect annihilationtopological defectumbilicuniversal scaling law |
| spellingShingle | Ingo Dierking Adam Moyle Gabriele Maria Cepparulo Katherine Skingle Laura Hernández Juhan Raidal Machine Learning Analysis of Umbilic Defect Annihilation in Nematic Liquid Crystals in the Presence of Nanoparticles Crystals liquid crystal nematic defect annihilation topological defect umbilic universal scaling law |
| title | Machine Learning Analysis of Umbilic Defect Annihilation in Nematic Liquid Crystals in the Presence of Nanoparticles |
| title_full | Machine Learning Analysis of Umbilic Defect Annihilation in Nematic Liquid Crystals in the Presence of Nanoparticles |
| title_fullStr | Machine Learning Analysis of Umbilic Defect Annihilation in Nematic Liquid Crystals in the Presence of Nanoparticles |
| title_full_unstemmed | Machine Learning Analysis of Umbilic Defect Annihilation in Nematic Liquid Crystals in the Presence of Nanoparticles |
| title_short | Machine Learning Analysis of Umbilic Defect Annihilation in Nematic Liquid Crystals in the Presence of Nanoparticles |
| title_sort | machine learning analysis of umbilic defect annihilation in nematic liquid crystals in the presence of nanoparticles |
| topic | liquid crystal nematic defect annihilation topological defect umbilic universal scaling law |
| url | https://www.mdpi.com/2073-4352/15/3/214 |
| work_keys_str_mv | AT ingodierking machinelearninganalysisofumbilicdefectannihilationinnematicliquidcrystalsinthepresenceofnanoparticles AT adammoyle machinelearninganalysisofumbilicdefectannihilationinnematicliquidcrystalsinthepresenceofnanoparticles AT gabrielemariacepparulo machinelearninganalysisofumbilicdefectannihilationinnematicliquidcrystalsinthepresenceofnanoparticles AT katherineskingle machinelearninganalysisofumbilicdefectannihilationinnematicliquidcrystalsinthepresenceofnanoparticles AT laurahernandez machinelearninganalysisofumbilicdefectannihilationinnematicliquidcrystalsinthepresenceofnanoparticles AT juhanraidal machinelearninganalysisofumbilicdefectannihilationinnematicliquidcrystalsinthepresenceofnanoparticles |