526 citations · 1.3k across the 11 of their papers we have counts for
12 papers
Heat Transport Mechanisms in MOF-5
Zezhu Zeng, Xia Liang, Hengtai Zheng +2
We find that MOF-5 supports both long-lived phonons and linker vibrations whose split spectra signal a breakdown of the phonon quasiparticle approximation. Combining mode-projected…
Nuclear quantum effects in the thermal conductivity of solid hydrogen
Hengtai Zheng, Zezhu Zeng, David E. Manolopoulos
We use a combination of path integral and lattice dynamics methods to calculate the thermal conductivity of solid parahydrogen. Path integral molecular dynamics is first used to ca…
Economised path integrals
Zezhu Zeng, David E. Manolopoulos
The Hessian of the ring polymer spring potential in the standard Trotter path integral is a symmetric circulant matrix with a centroid eigenvalue of zero. All such matr…
Lattice thermal transport from phonon spectra beyond perturbation theory
Zezhu Zeng, Michele Simoncelli, David E. Manolopoulos
We develop a molecular dynamics framework to compute the mode-resolved phonon spectral density from classical correlations of an annihilation-like phonon variable. For harmonic osc…
Thermal transport of amorphous hafnia across the glass transition
Zezhu Zeng, Xia Liang, Zheyong Fan +3
Heat transport in glasses across a wide range of temperature is vital for applications in gate dielectrics and heat insulator. However, it remains poorly understood due to the chal…
Lattice distortion leads to glassy thermal transport in crystalline CsBiICl
Zezhu Zeng, Zheyong Fan, Michele Simoncelli +5
The glassy thermal conductivities observed in crystalline inorganic perovskites such as CsBiICl is perplexing and lacking theoretical explanations. Here, we first e…
Molecular dynamics simulations of heat transport using machine-learned potentials: A mini review and tutorial on GPUMD with neuroevolution potentials
Haikuan Dong, Yongbo Shi, Penghua Ying +9
Molecular dynamics (MD) simulations play an important role in understanding and engineering heat transport properties of complex materials. An essential requirement for reliably pr…
General-purpose machine-learned potential for 16 elemental metals and their alloys
Keke Song, Rui Zhao, Jiahui Liu +25
Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicabil…
Pushing thermal conductivity to its lower limit in crystals with simple structures
Zezhu Zeng, Xingchen Shen, Ruihuan Cheng +7
Materials with low thermal conductivity usually have complex crystal structures. Herein we experimentally find that a simple crystal structure material AgTlI2 (I4/mcm) owns an extr…
Water dissociation on pristine low-index TiO2 surfaces
Zezhu Zeng, Felix Wodaczek, Keyang Liu +4
Water absorption and dissociation processes on pristine low-index TiO interfaces are important but poorly understood outside the well-studied anatase (101) and rutile (110). To…
GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
Zheyong Fan, Yanzhou Wang, Penghua Ying +17
We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in [Fan et al., Phys. Rev. B 104, 104309 (2…
Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport
Zheyong Fan, Zezhu Zeng, Cunzhi Zhang +4
We develop a neuroevolution-potential (NEP) framework for generating neural network based machine-learning potentials. They are trained using an evolutionary strategy for performin…