[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 …
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 …
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 …
Tensorial properties via the neuroevolution potential framework: Fast simulation of infrared and Raman spectra
N Xu, P Rosander, C Schäfer… - Journal of Chemical …, 2024 - ACS Publications
Infrared and Raman spectroscopy are widely used for the characterization of gases, liquids,
and solids, as the spectra contain a wealth of information concerning, in particular, the …
and solids, as the spectra contain a wealth of information concerning, in particular, the …
[HTML][HTML] Comparing machine learning potentials for water: Kernel-based regression and Behler–Parrinello neural networks
In this paper, we investigate the performance of different machine learning potentials (MLPs)
in predicting key thermodynamic properties of water using RPBE+ D3. Specifically, we …
in predicting key thermodynamic properties of water using RPBE+ D3. Specifically, we …
[HTML][HTML] Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials
A Omranpour, P Montero De Hijes, J Behler… - The Journal of …, 2024 - pubs.aip.org
As the most important solvent, water has been at the center of interest since the advent of
computer simulations. While early molecular dynamics and Monte Carlo simulations had to …
computer simulations. While early molecular dynamics and Monte Carlo simulations had to …
Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials
We propose an efficient approach for simultaneous prediction of thermal and electronic
transport properties in complex materials. Firstly, a highly efficient machine-learned …
transport properties in complex materials. Firstly, a highly efficient machine-learned …
Random sampling versus active learning algorithms for machine learning potentials of quantum liquid water
Training accurate machine learning potentials requires electronic structure data
comprehensively covering the configurational space of the system of interest. As the …
comprehensively covering the configurational space of the system of interest. As the …
Thermal transport across TiO2–H2O interface involving water dissociation: Ab initio-assisted deep potential molecular dynamics
Water dissociation on TiO 2 surfaces has been known for decades and holds great potential
in various applications, many of which require a proper understanding of thermal transport …
in various applications, many of which require a proper understanding of thermal transport …