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Data-driven algorithms for materials innovation


Materials informatics, Machine learning, Natural Language Processing


Development of machine learning algorithms for materials design and discovery.
Automatic construction of materials databases from related research papers using natural language processing.
Development of profiling serivce for mateials science reserchers.
Prediction of materials properties using regression methods.


Development of MDTS (Materials Design by Tree search) Python package for materials space exploration based on Monte Carlo tree search (MCTS) (
Development of visualization service for NIMS SAMURAI researchers catalouge to capture research output and connect researchers with similar reserach interest using natural language processing.



Materials design and discovery can be represented as selecting the optimal structure from a space of candidates that optimizes a target property. The selection is an iterative process, where selected candidate in one iteration is evaluted and fedback to the process for a more informed selection in the next iteration. Since the number of candidates can be exponentially proportional to the structure determination variables, the efficiency to obtain the optimal structure is a critical issue. We use Monte Carlo tree search (MCTS) approach in combination with expansion policy neural network to accelerate this process. MCTS has no tuning parameters and works autonomously in various problems. Additionally, it does not require pre- availalbe training data and it is highly scalble making it good fit for large spaces problems such as the chemical and mateirals space.


Optimization of the chemical compositions and heat treatment scheduling for new Ni-based superalloy for addiditve manufacturing using machine learning. Active-Leaning-Driven Development of Platinum-Free Electrocatalysts for the Hydrogen Oxidation Reaction.