[HTML][HTML] Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials
Molecular dynamics (MD) simulations play an important role in understanding and
engineering heat transport properties of complex materials. An essential requirement for …
engineering heat transport properties of complex materials. An essential requirement for …
Sub-micrometer phonon mean free paths in metal–organic frameworks revealed by machine learning molecular dynamics simulations
Metal–organic frameworks (MOFs) are a family of materials that have high porosity and
structural tunability and hold great potential in various applications, many of which require a …
structural tunability and hold great potential in various applications, many of which require a …
Accurate prediction of heat conductivity of water by a neuroevolution potential
We propose an approach that can accurately predict the heat conductivity of liquid water. On
the one hand, we develop an accurate machine-learned potential based on the …
the one hand, we develop an accurate machine-learned potential based on the …
Mechanisms of temperature-dependent thermal transport in amorphous silica from machine-learning molecular dynamics
Amorphous silica (a-SiO 2) is a foundational disordered material for which the thermal
transport properties are important for various applications. To accurately model the …
transport properties are important for various applications. To accurately model the …
Vibrational anharmonicity results in decreased thermal conductivity of amorphous at high temperature
While the high-temperature thermal transport in crystalline materials has been recently
carefully addressed, it is much less explored for amorphous materials. Most of the existing …
carefully addressed, it is much less explored for amorphous materials. Most of the existing …
Machine learning based modeling of disordered elemental semiconductors: understanding the atomic structure of a-Si and aC
MA Caro - Semiconductor Science and Technology, 2023 - iopscience.iop.org
Disordered elemental semiconductors, most notably aC and a-Si, are ubiquitous in a myriad
of different applications. These exploit their unique mechanical and electronic properties. In …
of different applications. These exploit their unique mechanical and electronic properties. In …
Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamics
Machine learned potentials (MLPs) have been widely employed in molecular dynamics
simulations to study thermal transport. However, the literature results indicate that MLPs …
simulations to study thermal transport. However, the literature results indicate that MLPs …
Understanding Defects in Amorphous Silicon with Million‐Atom Simulations and Machine Learning
The structure of amorphous silicon (a‐Si) is widely thought of as a fourfold‐connected
random network, and yet it is defective atoms, with fewer or more than four bonds, that make …
random network, and yet it is defective atoms, with fewer or more than four bonds, that make …
[HTML][HTML] Development of a neuroevolution machine learning potential of Pd-Cu-Ni-P alloys
R Zhao, S Wang, Z Kong, Y Xu, K Fu, P Peng, C Wu - Materials & Design, 2023 - Elsevier
Abstract Pd-Cu-Ni-P alloy is an ideal model system of metallic glass known for its
exceptional glass-forming ability. However, few correlation of structures with properties was …
exceptional glass-forming ability. However, few correlation of structures with properties was …
Molecular dynamics simulation of thermal, hydraulic, and mechanical properties of bentonite clay at 298 to 373 K
Bentonite, a fine-grained geologic material rich in smectite clay, is considered for use in the
isolation of high-level radioactive waste (HLRW) because of its low hydraulic permeability …
isolation of high-level radioactive waste (HLRW) because of its low hydraulic permeability …