Ingo Dierking

Biographie:
Ingo Dierking received his PhD in 1995 from the University of Clausthal in Germany. After a postdoc at the IBM TJ Watson Research Center, he joined Chalmers University in Sweden where he was appointed docent, before joining the University of Darmstadt as lecturer and the University of Manchester in 2002 as senior lecturer. He has published more than 180 scientific papers, as well as several books, and is the 2009 awardee of the Hilsum medal, the 2016 recipient of the Samsung Mid-Career Award for Research Excellence, the 2021 recipient of the G.W. Gray Medal, and was awarded the Luckhurst-Samulski prize in 2023. Dierking is the former President of the International Liquid Crystal Society (ILCS) and the former Chair of the British Liquid Crystal Society (BLCS). His research interests lie in the field of soft matter physics with an emphasis on liquid crystals and liquid crystal-based composites, as well as solitons and machine learning applied to soft matter.

Titre de la communication:
Characterizing Liquid Crystal Phases via Textures and Machine Learning

À propos de cette session tutorielle:
Since the beginning of liquid crystal (LC) research in 1888 polarizing optical microscopy (POM) has been one of the workhorses for the characterization of liquid crystalline materials. Helped by the fact that defects and director fields are easily formed and deformed due to very small elastic constants, typical textures are often observed for specific phases. Nowadays, POM is obviously supported by further experimental methods, such as differential scanning calorimetry (DSC) and X-ray diffraction (XRD), both wide-angle and small-angle scattering (WAXS and SAXS). For some classes of LCs other methods like nuclear magnetic resonance (NMR), spectroscopy and dielectric spectroscopy or switching experiments are suitable to differentiate between different phases. In very recent years computational methods based on machine learning are increasingly employed for the characterization of mesophases. 

Placing an emphasis on the polarization optical appearance of liquid crystals, thus their textures with and without boundary conditions imposed by substrates, we will discuss the observation and characterization of the large number of occurring LC phases for calamitic, discotic and bent-core molecules, together with chiral frustrated phases, variants of polar fluid smectic phases and the recently discovered nematic variants. Occasional examples of DSC and XRD will be provided and the machine learning characterization of orthogonal phases, chiral phases, ferroelectric phases and nematic sub-variants will be demonstrated via convolutional neural networks as well as inception models. 

At last, lyotropic textures will be shown for amphiphilic molecules, inorganic colloidal systems, 2D materials, dyes and some macromolecules, including biological macromolecules such as DNA or cellulose nanocrystals.