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[HTML][HTML] Recent advances in thermal-conductive insulating polymer composites with various fillers
Q Chen, K Yang, Y Feng, L Liang, M Chi… - Composites Part A …, 2024 - Elsevier
Abstract Development of polymer-based composites with excellent thermal conductivity and
electrical insulation properties is a hot research topic, because more and more electrical …
electrical insulation properties is a hot research topic, because more and more electrical …
Data‐driven materials innovation and applications
Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …
experimental and computational investigative methodologies, the massive amounts of data …
Unveiling thermal stresses in RETaO4 (RE= Nd, Sm, Eu, Gd, Tb, Dy, Ho and Er) by first-principles calculations and finite element simulations
Thermal stress (σ) plays a critical role in regulating the stability and durability of thermal
barrier coatings (TBCs) during service. However, its measurements are limited due to …
barrier coatings (TBCs) during service. However, its measurements are limited due to …
Emerging materials intelligence ecosystems propelled by machine learning
The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its
successes and promises, several AI ecosystems are blossoming, many of them within the …
successes and promises, several AI ecosystems are blossoming, many of them within the …
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 …
AI methods in materials design, discovery and manufacturing: A review
In the advent of the digital revolution, Artificial Intelligence (AI) has emerged as a pivotal tool
in various domains, including materials design and discovery. This paper provides a …
in various domains, including materials design and discovery. This paper provides a …
Machine-learning and high-throughput studies for high-entropy materials
The combination of multiple-principal element materials, known as high-entropy materials
(HEMs), expands the multi-dimensional compositional space to gigantic stoichiometry. It is …
(HEMs), expands the multi-dimensional compositional space to gigantic stoichiometry. It is …
A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil
Thermal conductivity is a specific thermal property of soil which controls the exchange of
thermal energy. If predicted accurately, the thermal conductivity of soil has a significant effect …
thermal energy. If predicted accurately, the thermal conductivity of soil has a significant effect …
Predicting the thermal conductivity of soils using integrated approach of ANN and PSO with adaptive and time-varying acceleration coefficients
This study aims to propose hybrid adaptive neuro swarm intelligence (HANSI) techniques for
predicting the thermal conductivity of unsaturated soils. The novel contribution is made by …
predicting the thermal conductivity of unsaturated soils. The novel contribution is made by …
Charting lattice thermal conductivity for inorganic crystals and discovering rare earth chalcogenides for thermoelectrics
Thermoelectric power generation represents a promising approach to utilize waste heat. The
most effective thermoelectric materials exhibit low thermal conductivity κ. However, less than …
most effective thermoelectric materials exhibit low thermal conductivity κ. However, less than …