Hitachi Materials Informatics Analytics Platform Assisting Rapid Development Yoshihiro Osakabe, Akinori Asahara, Hidekazu Morita Hitachi Ltd. 1-6-1, Marunouchi, Chiyoda-ku, Tokyo 100-8220, Japan {yoshihiro.osakabe.fj@hitachi.com} Abstract The data science platform for materials developments is demonstrated. Due to the recent great advances in artificial in- telligence, it becomes more realistic that the industrial appli- cation of materials informatics (MI) which is the data-driven approach to discover and investigate materials characteristics. However, it is not quite easy for materials manufacturers to set up MI analytics environments without any help. There- fore, we provide the user-friendly cloud-based IT platform for non-experts of IT enabling materials scientists in R&D departments to analyze their experimental data effectively for rapid developments. Motivation Product developments require significant time and costs to find the optimal combination of ingredients and parame- ters. Materials Informatics (MI) is an emerging study field based on the both informatics and materials science, with the goal of greatly reducing the resources and risks required to discover, invest, and deploy new materials (Curtarolo et al. 2013). Recently, artificial intelligence (AI) has improved the MI performance, thus the experimental candidates can be narrowed down without unnecessary trials and errors before its actual experiments to discover or create new materials with yet-to-be realized properties. In fact, US government has invested over $250 million to assist MI projects (Ma- terials Genome Initiative 2011). The Novel Materials Dis- covery Laboratory in EU also opens new oppotunities to in- vestigating MI by delivering analytics tools and open access repository of materials data (NOMAD Laboratory 2015). According to such outreach activities, there has been heavy demands of materials manufacturers for introducing MI- powered methodology into their R&D processes to increase their industrial competitiveness, and the number of startups in MI analytics services is increasing. In figure 1, the concept of this demonstration is illustrated. In many cases, it is difficult for materials scientists to select suitable preprocessing method and effective algorithm to Copyright c  2020 held by the author(s). In A. Martin, K. Hinkel- mann, H.-G. Fill, A. Gerber, D. Lenat, R. Stolle, F. van Harmelen (Eds.), Proceedings of the AAAI 2020 Spring Symposium on Com- bining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020). Stanford University, Palo Alto, California, USA, March 23-25, 2020. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). solve their problems because they do not have enough infor- matics knowledge, which means that they need the supports of informatics experts (data scientists). Their relation can be understood as that between a runner and his escort, thus this phase can be regarded as an “accompanying phase.” Though this service style is common, it may remain the possibil- ity that the informatics experts can not exactly understand the characteristics of target materials and obtain the know- hows materials scientists have. This problem will be solved if materials scientists can reach analysis results by them- selves without the excessive IT and analytics knowledge. That phase can be understood as a “self-managing phase,” and the MI analytics services should be shifted to that phase from accompanying phase for scaling up and rapid prototyp- ing. It suggests the need of the informatics expert alternative and one-stop platform for storing, analyzing data and visu- alizing analysis results. Figure 1: The concept of MIAP (MI Analytics Platform)