[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 …
Machine learned force-fields for an Ab-initio quality description of metal-organic frameworks
Metal-organic frameworks (MOFs) are an incredibly diverse group of highly porous hybrid
materials, which are interesting for a wide range of possible applications. For a meaningful …
materials, which are interesting for a wide range of possible applications. For a meaningful …
Machine learning interatomic potentials for amorphous zeolitic imidazolate frameworks
The detailed understanding of the microscopic structure of amorphous phases of metal–
organic frameworks (MOFs) remains a widely open question: characterization of these …
organic frameworks (MOFs) remains a widely open question: characterization of these …
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 …
Mathematically inspired structure design in nanoscale thermal transport
Mathematically inspired structure design has emerged as a powerful approach for tailoring
material properties, especially in nanoscale thermal transport, with promising applications …
material properties, especially in nanoscale thermal transport, with promising applications …
Quantum-accurate machine learning potentials for metal-organic frameworks using temperature driven active learning
Understanding structural flexibility of metal-organic frameworks (MOFs) via molecular
dynamics simulations is crucial to design better MOFs. Density functional theory (DFT) and …
dynamics simulations is crucial to design better MOFs. Density functional theory (DFT) and …
Machine learning potential for modelling H 2 adsorption/diffusion in MOFs with open metal sites
Metal–organic frameworks (MOFs) incorporating open metal sites (OMS) have been
identified as promising sorbents for many societally relevant-adsorption applications …
identified as promising sorbents for many societally relevant-adsorption applications …
Active learning molecular dynamics-assisted insights into ultralow thermal conductivity of two-dimensional covalent organic frameworks
Two-dimensional covalent organic frameworks (2D COFs) are novel materials with high
porosities and large surface areas that are highly sought after for separation technologies …
porosities and large surface areas that are highly sought after for separation technologies …
Unravelling abnormal in-plane stretchability of two-dimensional metal–organic frameworks by machine learning potential molecular dynamics
Two-dimensional (2D) metal–organic frameworks (MOFs) hold immense potential for
various applications due to their distinctive intrinsic properties compared to their 3D …
various applications due to their distinctive intrinsic properties compared to their 3D …