23 citations · 80 across the 12 of their papers we have counts for
5 papers · 1 filter
Metavalent Bonding-Induced Phonon Hardening and Giant Anharmonicity in BeO
Xuejie Li, Yuzhou Hao, Yujie Liu +6
The search for materials with intrinsically low thermal conductivity () is critical for energy applications, yet conventional descriptors often fail to capture the complex int…
Machine learning-driven elasticity prediction in advanced inorganic materials via convolutional neural networks
Yujie Liu, Zhenyu Wang, Hang Lei +6
Inorganic crystal materials have broad application potential due to excellent physical and chemical properties, with elastic properties (shear modulus, bulk modulus) crucial for pr…
Same-group element replacement enhances superconductivity in clathrate-like YH4
Xuejie Li, Yuzhou Hao, Yujie Liu +5
H3S, LaH10, and hydrogen-based compounds have garnered significant interest due to their high-temperature superconducting properties. However, the requirement for extremely high pr…
PINK: physical-informed machine learning for lattice thermal conductivity
Yujie Liu, Xiaoying Wang, Yuzhou Hao +5
Lattice thermal conductivity () is crucial for efficient thermal management in electronics and energy conversion technologies. Traditional methods for predicting \k{appa}L are…
Semantic Embeddings of Chemical Elements for Enhanced Materials Inference and Discovery
Yunze Jia, Yuehui Xian, Yangyang Xu +5
We present a framework for generating universal semantic embeddings of chemical elements to advance materials inference and discovery. This framework leverages ElementBERT, a domai…