Charge self-consistent dynamical mean field theory calculations incombination with linear combination of numerical atomic orbitalsframework based density functional theory

We present a formalism of charge self-consistent dynamical mean field theory(DMFT)in combination with densityfunctional theory(DFT)within the linear combination of numerical atomic orbitals(LCNAO)framework.We implementedthe charge self-consistent DFT+DMFT formalism by interfacing a full-potential all-electron DFT code with threehybridization expansion-based continuous-time quantum Monte Carlo impurity solvers.The benchmarks on several 3d,4fand 5f strongly correlated electron systems validated our formalism and implementation.Furthermore,within the LCANOframework,our formalism is general and the code architecture is extensible,so it can work as a bridge merging differentLCNAO DFT packages and impurity solvers to do charge self-consistent DFT+DMFT calculations.

Half-metallic ferromagneticWeyl fermions related to dynamic correlations in the zinc-blende compound VAs

The realization of 100%polarized topologicalWeyl fermions in half-metallic ferromagnets is of particular importance for fundamental research and spintronic applications.Here,we theoretically investigate the electronic and topological properties of the zinc-blende compound VAs,which was deemed as a half-metallic ferromagnet related to dynamic correlations.Based on the combination of density functional theory and dynamical mean field theory,we uncover that the half-metallic ferromagnet VAs exhibits attractive Weyl semimetallic behaviors which are very close to the Fermi level in the DFT+U regime with effect U values ranging from 1.5 eV to 2.5 eV.Meanwhile,we also investigate the magnetization-dependent topological properties;the results show that the change of magnetization directions only slightly affects the positions of Weyl points,which is attributed to the weak spin–orbital coupling effects.The topological surface states of VAs projected on semi-infinite(001)and(111)surfaces are investigated.The Fermi arcs of all Weyl points are clearly visible on the projected Fermi surfaces.Our findings suggest that VAs is a fully spin-polarized Weyl semimetal with many-body correlated effects in the effective U values range from 1.5 eV to 2.5 eV.

Fast impurity solver for dynamical mean field theory based on second order perturbation around the atomic limit

This paper proposes an impurity solver for the dynamical mean field theory （DMFT） study of the Mott insulators, which is based on the second order perturbation of the hybridization function. After careful benchmarking with quantum Monte Carlo results on the anti-ferromagnetic phase of the Hubbard model, it concludes that this impurity solver can capture the main physical features in the strong coupling regime and can be a very useful tool for the LDA （local density approximation） ＋ DMFT studies of the Mort insulators with long range order.

Generalization of iterative perturbation theory and coherent potential approximation (ITP+CPA) to double exchange model with orbital degeneracy

A combination of the iterative perturbation theory （ITP） of the dynamical mean field theory （DMFT） and coherentpotential approximation （CPA） is generalized to the double exchange model with orbital degeneracy. The Hubbard interaction and the off-diagonal components for the hopping matrix tij^mn（m ≠ n） are considered in our calculation of spectrum and optical conductivity. The numerical results show that the effects of the non-diagonal hopping matrix elements are important.

Effect of interaction and temperature on quantum phase transition in anisotropic square-octagon lattice

We investigate the effect of interaction, temperature, and anisotropic parameter on the quantum phase transitions in an anisotropic square-octagon lattice with fermions under the framework of the single band Hubbard model through using the combination of cellular dynamical mean field theory and a continuous time Monte Carlo algorithm. The competition between interaction and temperature shows that with the increase of the anisotropic parameter, the critical on-site repulsive interaction for the metal-insulator transition increases for fixed temperature. The interaction-anisotropic parameter phase diagram reveals that with the decrease of temperature, the critical anisotropic parameter for the Mott transition will increase for fixed interaction cases.

Long-Range Ordered Nanostructures of Assembling Macromolecules via Rational Design of Kinetic Pathways:A Computational Perspective

Designing the kinetic pathways of assembling macromolecules such as block copolymers and DNA strands is crucial not only for an achievement of thermodynamically equilibrium nanostructures over macroscopic areas,but also for a better understanding of formation process of higher-level superstructures where well-tailored assemblies act as mesoscopic building units.Theoretical analysis and computer simulations provide excellent opportunities to microscopically reveal the kinetics and mechanism of structural evolution as well as the collective behaviors of building units.In this perspective,we summarize our efforts of theoretical and computational modelling to understand the long-range ordering mechanisms and the organization kinetics of assembling macromolecules along designable pathways.First,we present the computational modelling and recent strategies of designable pathways for the achievement of long-range ordering.Then,from the computational views,we give the applications of pathway-designed strategies to explore the ordering mechanism and kinetics in the course of structural evolution,covering the block copolymers and their nanocomposites under zone annealing as well as the hierarchical self-assembly of mesoscopic building units(e.g.,patchy micelles and DNA-functionalized nanoparticles).Finally,we outlook future directions in the field of designable pathways for the achievement of long-range ordered nanostructures.This perspective could promote further efforts towards the wide applications of theoretical and computational modelling in the construction of soft hybrid metamaterials.

Inferring the Physics of Structural Evolution of Multicomponent Polymers via Machine-Learning-Accelerated Method

Dynamic self-consistent field theory(DSCFT)is a fruitful approach for modeling the structural evolution and collective kinetics for a wide variety of multicomponent polymers.However,solving a set of DSCFT equations remains daunting because of high computational demand.Herein,a machine learning method,integrating low-dimensional representations of microstructures and long short-term memory neural networks,is used to accelerate the predictions of structural evolution of multicomponent polymers.It is definitively demonstrated that the neural-network-trained surrogate model has the capability to accurately forecast the structural evolution of homopolymer blends as well as diblock copolymers,without the requirement of“on-the-fly”solution of DSCFT equations.Importantly,the data-driven method can also infer the latent growth laws of phase-separated microstructures of multicomponent polymers through simply using a few of time sequences from their past,without the prior knowledge of the governing dynamics.Our study exemplifies how the machine-learning-accelerated method can be applied to understand and discover the physics of structural evolution in the complex polymer systems.