First-principles based modeling on phonon dynamics and transport using density functional theory and Boltzmann transport equation has proven powerful in predicting thermal conductivity of crystalline materials, but it remains unfeasible for modeling complex crystals and disordered solids due to the prohibitive computational cost to capture the disordered structure, especially when the quasiparticle "phonon" model breaks down. Recently, machine-learning regression algorithms show great promises for building high-accuracy potential fields for atomistic modeling with length and time scales far beyond those achievable by first-principles calculations. In this work, using both crystalline and amorphous silicon as examples, we develop machine learning based potential fields for predicting thermal conductivity. The machine learning based interatomic potential is derived from density functional theory calculations by stochastically sampling the potential energy surface in the configurational space. The thermal conductivities of both amorphous and crystalline silicon are then calculated using equilibrium molecular dynamics, which agree well with experimental measurements. This work documents the procedure for training the machine-learning based potentials for modeling thermal conductivity, and demonstrates that machine-learning based potential can be a promising tool for modeling thermal conductivity of both crystalline and amorphous materials with strong disorder.
After the significant discovery of the hole-doped nickelate compound Nd0.8Sr0.2NiO2, an analysis of the electronic structure, orbital components, Fermi surfaces and band topology could be helpful to understand the mechanism of its superconductivity. Based on the first-principles calculations, we find that Ni $3d_{x^2-y^2}$ states contribute the largest Fermi surface. $Ln~5d_{3z^2-r^2}$ states form an electron pocket at Γ, while 5dxy states form a relatively bigger electron pocket at A. These Fermi surfaces and symmetry characteristics can be reproduced by our two-band model, which consists of two elementary band representations: B1g@1a ⊕ A1g@1b. We find that there is a band inversion near A, giving rise to a pair of Dirac points along M–A below the Fermi level upon including spin-orbit coupling. Furthermore, we have performed the DFT+Gutzwiller calculations to treat the strong correlation effect of Ni 3d orbitals. In particular, the bandwidth of $3d_{x^2-y^2}$ has been renormalized largely. After the renormalization of the correlated bands, the Ni 3dxy states and the Dirac points become very close to the Fermi level. Thus, a hole pocket at A could be introduced by hole doping, which may be related to the observed sign change of Hall coefficient. By introducing an additional Ni 3dxy orbital, the hole-pocket band and the band inversion can be captured in our modified model. Besides, the nontrivial band topology in the ferromagnetic two-layer compound La3Ni2O6 is discussed and the band inversion is associated with Ni $3d_{x^2-y^2}$ and La 5dxy orbitals.
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