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 guided 3D printing of carbon microlattices with customized performance for supercapacitive energy storage
H Yang, L Fang, Z Yuan, X Teng, H Qin, Z He, Y Wan… - Carbon, 2023 - Elsevier
Abstract Three-dimensional (3D) printing has stood out as a reliable technology to construct
carbon microlattice electrodes for supercapacitors (SCs) in the field of custom areal …
carbon microlattice electrodes for supercapacitors (SCs) in the field of custom areal …
Hydrogenated graphene with tunable poisson's ratio using machine learning: implication for wearable devices and strain sensors
The Poisson's ratio of two-dimensional materials such as graphene can be tailored by
surface hydrogenation. The density and distribution of hydrogenation may significantly affect …
surface hydrogenation. The density and distribution of hydrogenation may significantly affect …
Exploring the rare-earth zirconate ceramics RE2Zr2O7 with ultralow thermal conductive through an interpretable machine learning
Thermal barrier coatings (TBCs) have a unique advantage in aviation engines and industrial
gas turbines due to their low thermal conductivity (TC). Rare-earth zirconate ceramics RE 2 …
gas turbines due to their low thermal conductivity (TC). Rare-earth zirconate ceramics RE 2 …
Study of the novel boron nitride polymorphs: First-principles calculations and machine learning
Q Fan, W Li, N Wu, Y Zhao, Y Song, X Yu… - Chinese Journal of …, 2024 - Elsevier
This study explores two new boron nitride polymorphs, namely C222 1 BN and I-4 BN,
characterized by sp 2 hybridization. The investigation is conducted through first-principles …
characterized by sp 2 hybridization. The investigation is conducted through first-principles …
[HTML][HTML] Estimating the lattice thermal conductivity of AlCoCrNiFe high-entropy alloy using machine learning
The lattice thermal conductivity stands as a pivotal thermos-physical parameter of high-
entropy alloys; nonetheless, achieving precise predictions of the lattice thermal conductivity …
entropy alloys; nonetheless, achieving precise predictions of the lattice thermal conductivity …
[HTML][HTML] Using Machine Learning Techniques to Discover Novel Thermoelectric Materials
E Yildirim, ÖC Yelgel - New Materials and Devices for …, 2023 - intechopen.com
Thermoelectric materials can be utilized to build devices that convert waste heat to power or
vice versa. In the literature, the best-known thermoelectrics, however, are based on rare …
vice versa. In the literature, the best-known thermoelectrics, however, are based on rare …
Study of the Elastic Modulus of Boron Nitride Polymorphs: First-Principles Calculations and Machine Learning
Q Fan, N Wu, Y Zhao, Y Song, X Yu, S Yun - Available at SSRN 4465345 - papers.ssrn.com
Two new boron nitride polymorphs, C2221 BN and I-4 BN, with sp2 hybridization are
investigated in this work by first-principles calculations, including the structural properties …
investigated in this work by first-principles calculations, including the structural properties …
[PDF][PDF] Multi-scale modeling of two-dimensional layered nanomaterials built of carbon, boron, and nitrogen atoms
A Jamróz - 2022 - repozytorium.uw.edu.pl
Two-dimensional materials and their alloys remain under interest of scientists, due to their
very appealing and conceivably tunable properties. CxByN1− x− y graphenelike alloys …
very appealing and conceivably tunable properties. CxByN1− x− y graphenelike alloys …