Heavy rare-earth R2Co6Al20-δ single crystals adopt orthorhombic Imma structure with Al deficiency δ ~0.7-0.9 and exhibit AFM ordering (TN 1.8-11.8 K) with two transitions in Gd/Tb and clear deviation from de Gennes scaling.
Jeffrey and Toberer, Eric S
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cond-mat.mtrl-sci 3years
2026 3roles
background 1polarities
background 1representative citing papers
Machine learning models, especially certain deep neural networks, can predict lattice thermal conductivity with useful accuracy across different generalization tests while being orders of magnitude faster than first-principles calculations.
DFT study of novel K2SnGeX6 and Rb2SnGeX6 (X=Cl,Br,I) predicts cubic stability, direct bandgaps 0.64-1.44 eV, ductility, and ZT up to 2.4 at 1000 K for K2SnGeI6.
citing papers explorer
-
Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates
Machine learning models, especially certain deep neural networks, can predict lattice thermal conductivity with useful accuracy across different generalization tests while being orders of magnitude faster than first-principles calculations.