29 citations · 30 across the 4 of their papers we have counts for
6 papers · 1 filter
Quantitative control and recording of materials-synthesis processes using an automated experimentation platform
Yusuke Hashimoto, Takaya Muramoto, Hikari Terada +3
Data-driven materials development requires the collection of large amounts of high-quality materials data. Full autonomy of materials experiments is anticipated, but its technical…
Building a physics-aware AI ecosystem for solid-state hydrogen storage materials
Seong-Hoon Jang, Yiwen Yao, Chuanyu Liu +66
Hydrogen storage remains a central bottleneck for scalable hydrogen energy systems due to the multiscale and coupled nature of the thermodynamics, kinetics, and microstructural evo…
A unified descriptor framework for hydrogen storage capacity and equilibrium pressure in interstitial hydrides
Seong-Hoon Jang, Di Zhang, Xue Jia +9
Hydrogen is a promising energy carrier, yet its practical deployment is limited by the lack of storage materials that simultaneously achieve high storage capacity () and practic…
Quantifying chirality of phonons
Yu-Chi Huang, Gakuto Kusuno, Yusuke Hashimoto +3
Recent years have witnessed growing interest in chiral phonons, lattice vibrations carrying angular momentum and exhibiting handedness, as revealed by helicity-dependent optical ph…
Digital Hydrogen Platform (DigHyd): A Rigorously Curated Database for Hydrogen Storage Materials Empowered by AI-Assisted Literature Mining
Seong-Hoon Jang, Di Zhang, Xue Jia +8
Solid-state hydrogen storage materials are promising candidates for safe and compact hydrogen storage; however, data-driven discovery in this field remains limited by the availabil…
A Materials Map Integrating Experimental and Computational Data via Graph-Based Machine Learning for Enhanced Materials Discovery
Yusuke Hashimoto, Xue Jia, Hao Li +1
Materials informatics (MI), emerging from the integration of materials science and data science, is expected to significantly accelerate material development and discovery. The dat…