Learning physical properties of liquid crystals with deep convolutional neural networks.

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

SUBMITTER: Sigaki HYD 

PROVIDER: S-EPMC7203147 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature