This group aims to find new materials and create/control materials’ function using data science. The lab basically has worked on experimental researches, and thus we are using the data obtained by ourselves. Sometimes interacting with other group in the lab, other university, and research institutes.

1. Statistical modeling of photo-bending behavior

Statistical modeling
Statistical modeling constructs the strategy from the experimental observations of actuation behavior to the description as a mathematical equation. We have successfully modeled photo-bending behavior of hybrid silicone mixed with azobenzene powder [1]. Preferable use of the photo-bending property would be very gentle touch and grip of, for example, fragile objects. This group aims to promote the development and implementation of photo-responsive materials for innovative actuators.

2. Machine learning for material discovery

Machine learning is a method for obtaining knowledge for material development from a dataset, composed of a large number of material data. We are aiming to develop new materials by finding insights from data sets we accumulated so far.