[Kenta Hongo] Efficient searching of materials structure using Bayesian inference

Research Director

Kenta Hongo

Kenta Hongo

School of Information Science, Japan Advanced Institute of Science and Technology
Assistant Professor
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Outline

An ultimate dream in materials science is to computationally discover novel materials with desiarable properties. Recent first-principles simulations can predict properties accurately, but cannot discover such desirable materials all by themselves. The structure search can be regarded as inverse problems of the property predictions, for which Bayesian statistics is helpful. I am developing a new approach to material structure search based on Bayesian statistics with a chemical lugnuage model for generating chemical compounds. Combining the Bayesian approach with millions of learning data from first-principles simulations, the project intends to discover dye-sensitized solar cell with much higher efficiency than ever.

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