Predicting lattice thermal conductivity via machine learning: a mini review
Y Luo, M Li, H Yuan, H Liu, Y Fang - NPJ Computational Materials, 2023 - nature.com
Over the past few decades, molecular dynamics simulations and first-principles calculations
have become two major approaches to predict the lattice thermal conductivity (κ L), which …
have become two major approaches to predict the lattice thermal conductivity (κ L), which …
Machine learning and materials informatics approaches in the analysis of physical properties of carbon nanotubes: A review
LE Vivanco-Benavides, CL Martínez-González… - Computational Materials …, 2022 - Elsevier
Abstract Machine learning has proven to be technically flexible in recent years, which allows
it to be successfully implemented in problems in various areas of knowledge. Carbon …
it to be successfully implemented in problems in various areas of knowledge. Carbon …
General strategies to improve thermoelectric performance with an emphasis on tin and germanium chalcogenides as thermoelectric materials
Thermoelectric (TE) materials have attracted tremendous research interests over the past
few decades, due to their application in power generation technology from waste heat …
few decades, due to their application in power generation technology from waste heat …
Beyond T-graphene: Two-dimensional tetragonal allotropes and their potential applications
Breakthrough of graphene dictates that decreasing dimensionality of the semiconducting
materials can generate unusual electronic structures, excellent mechanical, and thermal …
materials can generate unusual electronic structures, excellent mechanical, and thermal …
Impressive thermoelectric figure of merit in two-dimensional tetragonal pnictogens: a combined first-principles and machine-learning approach
Over the past decade, two-dimensional materials have gained a lot of interest due to their
fascinating applications in the field of thermoelectricity. In this study, tetragonal monolayers …
fascinating applications in the field of thermoelectricity. In this study, tetragonal monolayers …
XTlO (X= K, Rb, Cs): Novel 2D semiconductors with high electron mobilities, ultra-low lattice thermal conductivities and high thermoelectric figures of merit at room …
W Fang, H Wei, X ** of superalkali and superhalogen on graphene quantum dot surfaces to enhance nonlinear optical response: An efficient strategy for fabricating novel electro …
Graphene quantum dots are evolving as a popular novel class of organic nonlinear optical
(NLO) materials, with applications in optoelectronics, nanocomposites, and many other …
(NLO) materials, with applications in optoelectronics, nanocomposites, and many other …
Ultrahigh strength and negative thermal expansion and low thermal conductivity in graphyne nanosheets confirmed by machine-learning interatomic potentials
After several decades of experimental endeavors, most recently large-area γ-graphyne
layered materials have been synthesized via a reversible dynamic alkyne metathesis …
layered materials have been synthesized via a reversible dynamic alkyne metathesis …
Two novel phases of germa-graphene: Prediction, electronic and transport applications
In search of potential candidates for thermoelectric energy conversion, graphene and its
derivatives always enthralled researchers for their exotic characteristics. Experimental …
derivatives always enthralled researchers for their exotic characteristics. Experimental …
First-principles and machine-learning study of electronic and phonon transport in carbon-based AA-stacked bilayer biphenylene nanosheets
In this study, the structural, electronic and thermal transport properties of AA-stacked bilayer
biphenylene sheet (BPN) are systematically investigated in the framework of first-principles …
biphenylene sheet (BPN) are systematically investigated in the framework of first-principles …