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
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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
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Physical properties of R$_2$Co$_6$Al$_{20-\delta}$ (R = Gd-Tm, Y) single crystals
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.
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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.
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First-Principles Study of Novel Lead-Free Double Perovskite \b{eta}2SnGeX6 (\b{eta} = K, Rb; X = Cl, Br, I) for thermomechanical, optoelectronic and outstanding thermoelectric applications
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.