Advances and applications of computational simulations in the inhibition of lithium dendrite growth
Z Xiao, R Yuan, T Zhao, Y Kuang, B Yin, C Liu, L Song - Ionics, 2023 - Springer
The lithium metal anode has attracted much attention from researchers because of its
extremely high theoretical capacity and most negative potential, but some problems caused …
extremely high theoretical capacity and most negative potential, but some problems caused …
Emergent opportunities with metallic alloys: from material design to optical devices
Metallic nanostructures and thin films are fundamental building blocks for next‐generation
nanophotonics. Yet, the fixed permittivity of pure metals often imposes limitations on the …
nanophotonics. Yet, the fixed permittivity of pure metals often imposes limitations on the …
A general tensor prediction framework based on graph neural networks
Graph neural networks (GNNs) have been shown to be extremely flexible and accurate in
predicting the physical properties of molecules and crystals. However, traditional invariant …
predicting the physical properties of molecules and crystals. However, traditional invariant …
Machine learning models for predicting the dielectric constants of oxides based on high-throughput first-principles calculations
Prediction models of both the electronic and ionic contributions to the static dielectric
constants have been constructed using data from density functional perturbation theory …
constants have been constructed using data from density functional perturbation theory …
[HTML][HTML] Comparative analysis of machine learning models for predicting dielectric properties in MoS2 nanofiller-reinforced epoxy composites
This research investigates the dielectric properties of nano epoxy composites by
incorporating various concentrations of MoS 2 into epoxy resin. The study explores the …
incorporating various concentrations of MoS 2 into epoxy resin. The study explores the …
[HTML][HTML] Machine learning approaches for permittivity prediction and rational design of microwave dielectric ceramics
Low permittivity microwave dielectric ceramics (MWDCs) are attracting great interest
because of their promising applications in the new era of 5G and IoT. Although theoretical …
because of their promising applications in the new era of 5G and IoT. Although theoretical …
Dielectric tensor prediction for inorganic materials using latent information from preferred potential
Z Mao, WW Li, J Tan - npj Computational Materials, 2024 - nature.com
Dielectrics are crucial for technologies like flash memory, CPUs, photovoltaics, and
capacitors, but public data on these materials are scarce, restricting research and …
capacitors, but public data on these materials are scarce, restricting research and …
Prediction of dielectric constants of ABO 3-type perovskites using machine learning and first-principles calculations
In this study, the machine-learning method, combined with density functional perturbation
theory (DFPT) calculations, was implemented to predict and validate the dielectric constants …
theory (DFPT) calculations, was implemented to predict and validate the dielectric constants …
Modeling the dielectric constants of crystals using machine learning
The relative permittivity of a crystal is a fundamental property that links microscopic chemical
bonding to macroscopic electromagnetic response. Multiple models, including analytical …
bonding to macroscopic electromagnetic response. Multiple models, including analytical …
Random Forest Regression Analysis for Estimating Dielectric Properties in Epoxy Composites Doped with Hybrid Nano Fillers
Bisphenol-A resin epoxy resin composites doped with various concentrations of inorganic
hybrid nanofillers, TiO2+ ZnO, were thoroughly examined in the research described in this …
hybrid nanofillers, TiO2+ ZnO, were thoroughly examined in the research described in this …