activity
20242026
most citedMachine learning for predicting ultralow thermal conductivity and high ZT in complex thermoelectric materials

23 citations · 63 across the 8 of their papers we have counts for

collaborators

9 papers

cond-mat.mtrl-sci2026

Four-phonon scattering and coherent heat transport in ultrawide-bandgap SrSnO3

Xuejie Li, Xiaoying Wang, Shengying Yue +4

SrSnO3 is a promising ultrawide-bandgap perovskite oxide whose thermal transport is governed by structural distortions and anharmonic lattice dynamics. Here, we investigate the lat…

cond-mat.mtrl-sci2026★ 2 cited

Role of octahedral tilting induced acoustic softening on limiting thermal transport in SrSnO3

Yuzhou Hao, Turab Lookman, Xiangdong Ding +2

Octahedral tilting is a fundamental structural distortion in perovskites, governing key phenomena such as lattice stabilizing, soft phonon dynamics, group-theoretical analysis, pha…

cond-mat.mtrl-sci2025

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…

cond-mat.mtrl-sci2025★ 1 cited

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…

cond-mat.supr-con2025

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…

cond-mat.mtrl-sci2025★ 7 cited

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…

cond-mat.mtrl-sci2024★ 10 cited

Copper delocalization leads to ultralow thermal conductivity in chalcohalide CuBiSeCl2

Yuzhou Hao, Junwei Che, Xiaoying Wang +5

Mixed anion halide-chalcogenide materials have attracted considerable attention due to their exceptional optoelectronic properties, making them promising candidates for various app…

cond-mat.mtrl-sci2024★ 20 cited

An interpretable formula for lattice thermal conductivity of crystals

Xiaoying Wang, Guoyu Shu, Guimei Zhu +5

Lattice thermal conductivity (kL) is a crucial physical property of crystals with applications in thermal management, such as heat dissipation, insulation, and thermoelectric energ…

cond-mat.mtrl-sci2024★ 23 cited

Machine learning for predicting ultralow thermal conductivity and high ZT in complex thermoelectric materials

Yuzhou Hao, Yuting Zuo, Jiongzhi Zheng +7

Efficient and precise calculations of thermal transport properties and figure of merit, alongside a deep comprehension of thermal transport mechanisms, are essential for the practi…