title: High dimensional data driven statistical mechanics.
abstract: In "3D4D materials science", there are five categories such as (a) Image acquisition, (b) Processing, (c) Analysis, (d) Modelling, and (e) Data sharing. This presentation highlights the core of these categories [1]. Analysis and modellingA three-dimensional (3D) microstructure image contains topological features such as connectivity in addition to metric features. Such more microstructural information seems to be useful for more precise property prediction. There are two ways for microstructure-based property prediction (Fig. 1A). One is 3D image data based modelling such as micromechanics or crystal plasticity finite element method. The other one is a numerical microstructural features driven machine learning approach such as artificial neural network or Bayesian estimation method. It is the key to convert the 3D image data into numerals in order to apply the dataset to property prediction. As a numerical feature of microstructures, grain size, number of density, of particles, connectivity of particles, grain boundary connectivity, stacking degree, clustering etc. should be taken into consideration. These microstructural features are so-called "materials genome". Among those materials genome, we have to find out dominant factors to determine a focused property. The dominant factorzs are defined as "descriptor(s)" in high dimensional data driven statistical mechanics.jmicro;63/suppl_1/i4/DFU086F1F1DFU086F1Fig. 1.(a) A concept of 3D4D materials science. (b) Fully-automated serial sectioning 3D microscope "Genus_3D". (c) Materials Genome Archive (JSPS). Image acquisitionIt is important for researchers to choice a 3D microscope from various microscopes depending on a length-scale of a focused microstructure. There is a long term request to acquire a 3D microstructure image more conveniently. Therefore a fully automated serial sectioning 3D optical microscope "Genus_3D" (Fig. 1B) has been developed and nowadays it is commercially available. A user can get a good contrast image and more than one hundred sections in a day using Genus_3D, namely 3D image capturing is now a day or hour work rather than monthly work. Data sharingIt is also another important issue to archive all materials genome data including process and properties for modelling-assisted high-throughput materials design. Japan Society for the Promotion of Science (JSPS) 176 working group has announced to commence "Materials Genome Archive" service (Fig. 1C).
authors.affiliation: Department of Mechanical Engineering, Graduate School of Science and Engineering, Kagoshima University, Korimoto 1-21-24, Kagoshima, 890-0065, Japan.
authors.forename: Yoshitaka
authors.initials: Y
authors.lastname: Adachi
authors.affiliation: Department of Mechanical Engineering, Graduate School of Science and Engineering, Kagoshima University, Korimoto 1-21-24, Kagoshima, 890-0065, Japan.
authors.forename: Sunao
authors.initials: S
authors.lastname: Sadamatsu