168 citations · 986 across the 21 of their papers we have counts for
9 papers · 1 filter
Machine Learning Guided Discovery of Gigantic Magnetocaloric Effect in HoB Near Hydrogen Liquefaction Temperature
Pedro Baptista de Castro, Kensei Terashima, Takafumi D Yamamoto +8
Magnetic refrigeration exploits the magnetocaloric effect which is the entropy change upon application and removal of magnetic fields in materials, providing an alternate path for…
Leveraging Legacy Data to Accelerate Materials Design via Preference Learning
Xiaolin Sun, Zhufeng Hou, Masato Sumita +3
Machine learning applications in materials science are often hampered by shortage of experimental data. Integration with legacy data from past experiments is a viable way to solve…
Pressure-induced superconductivity in SnSb2Te4
Peng Song, Ryo Matsumoto, Zhufeng Hou +6
We report the discovery of a new superconductor from phase change materials SnSb2Te4. Single crystals of SnSb2Te4 were grown using a conventional melting-growth method. The sample…
Bayesian optimization of chemical composition: a comprehensive framework and its application to Fe-type magnet compounds
Taro Fukazawa, Yosuke Harashima, Zhufeng Hou +1
We propose a framework for optimization of the chemical composition of multinary compounds with the aid of machine learning. The scheme is based on first-principles calculation usi…
Stability Engineering of Halide Perovskite via Machine Learning
Zhenzhu Li, Qichen Xu, Qingde Sun +2
Perovskite stability is of the core importance and difficulty in current research and application of perovskite solar cells. Nevertheless, over the past century, the formability an…
Designing nanostructures for interfacial phonon transport via Bayesian optimization
Shenghong Ju, Takuma Shiga, Lei Feng +3
We demonstrate optimization of thermal conductance across nanostructures by developing a method combining atomistic Green's function and Bayesian optimization. With an aim to minim…