1 citations · 1 across the 1 of their papers we have counts for
6 papers
LLM-driven discovery for carbon allotropes with bond-network entropy
Yuzhou Hao, Yujie Liu, Xuejie Li +4
The discovery of novel carbon allotropes with tailored thermal and mechanical properties is critical for advanced thermal management. However, exploring the vast configurational sp…
Materials Informatics: Emergence To Autonomous Discovery In The Age Of AI
Turab Lookman, YuJie Liu, Zhibin Gao
This perspective explores the evolution of materials informatics, from its foundational roots in physics and information theory to its maturation through artificial intelligence (A…
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 in…
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 ar…