arXiv subjects

Haochong Zhang

Publications and source records attributed to Haochong Zhang.

4 recordsLinked to original sources

arXiv2025

ABACUS: An Electronic Structure Analysis Package for the AI Era

ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source software for first-principles electronic structure calculations and molecular dynamics simulations. It mainly features density functional theory (DFT) and molecular dynamics functions and is compatible with both plane-wave basis sets and numerical atomic orbital basis sets. ABACUS serves as a platform that facilitates the integration of various electronic structure methods, such as Kohn-Sham DFT, stochastic DFT, orbital-free DFT, and real-time time-dependent DFT, etc. In addition, with the aid of high-performance computing, ABACUS is designed to perform efficiently and provide massive amounts of first-principles data for generating general-purpose machine learning potentials, such as DPA models. Furthermore, ABACUS serves as an electronic structure platform that interfaces with several AI-assisted algorithms and packages, such as DeePKS-kit, DeePMD, DP-GEN, DeepH, DeePTB, HamGNN, etc.

cond-mat.mtrl-sci↗

arXiv2025

Dynamic Carrier Modulation via Nonlinear Acoustoelectric Transport in van der Waals Heterostructures

Dynamically manipulating carriers in van der Waals heterostructures could enable solid-state quantum simulators with tunable lattice parameters. A key requirement is forming deep potential wells to reliably trap excitations. Here, we report the observation of nonlinear acoustoelectric transport and dynamic carrier modulation in boron nitride-encapsulated graphene devices coupled to intense surface acoustic waves (SAWs) on LiNbO3 substrates. SAWs generate strong acoustoelectric current densities (JAE), transitioning from linear to nonlinear regimes with increasing SAW intensity. In the nonlinear regime, periodic carrier (electrons, holes, or their mixtures) stripes emerge. Using counter-propagating SAWs, we create standing SAWs (SSAWs) to dynamically manipulate charge distributions without static gates. The saturation of JAE, attenuation transitions, and tunable resistance peaks confirm strong carrier localization. These results establish SAWs as a powerful tool for controlling carrier dynamics in two-dimensional (2D) materials, paving the way for the development of time-dependent quantum systems and acoustic lattices for quantum simulation.

cond-mat.mes-hall↗

arXiv2024

GPU Acceleration of Numerical Atomic Orbitals-Based Density Functional Theory Algorithms within the ABACUS package

With the fast developments of high-performance computing, first-principles methods based on quantum mechanics play a significant role in materials research, serving as fundamental tools for predicting and analyzing various properties of materials. However, the inherent complexity and substantial computational demands of first-principles algorithms, such as density functional theory, limit their use in larger systems. The rapid development of heterogeneous computing, particularly General-Purpose Graphics Processing Units (GPGPUs), has heralded new prospects for enhancing the performance and cost-effectiveness of first-principles algorithms. We utilize GPGPUs to accelerate the electronic structure algorithms in Atomic-orbital Based Ab-initio Computation at USTC (ABACUS), a first-principles computational package based on the linear combination of atomic orbitals (LCAO) basis set. We design algorithms on GPGPU to efficiently construct and diagonalize the Hamiltonian of a given system, including the related force and stress calculations. The effectiveness of this computational acceleration has been demonstrated through calculations on twisted bilayer graphene with the system size up to 10,444 atoms.

cond-mat.mtrl-sci↗

arXiv2024

Towards Harmonization of SO(3)-Equivariance and Expressiveness: a Hybrid Deep Learning Framework for Electronic-Structure Hamiltonian Prediction

Deep learning for predicting the electronic-structure Hamiltonian of quantum systems necessitates satisfying the covariance laws, among which achieving SO(3)-equivariance without sacrificing the non-linear expressive capability of networks remains unsolved. To navigate the harmonization between equivariance and expressiveness, we propose a deep learning method synergizing two distinct categories of neural mechanisms as a two-stage encoding and regression framework. The first stage corresponds to group theory-based neural mechanisms with inherent SO(3)-equivariant properties prior to the parameter learning process, while the second stage is characterized by a non-linear 3D graph Transformer network we propose, featuring high capability on non-linear expressiveness. The novel combination lies in the point that, the first stage predicts baseline Hamiltonians with abundant SO(3)-equivariant features extracted, assisting the second stage in empirical learning of equivariance; and in turn, the second stage refines the first stage's output as a fine-grained prediction of Hamiltonians using powerful non-linear neural mappings, compensating for the intrinsic weakness on non-linear expressiveness capability of mechanisms in the first stage. Our method enables precise, generalizable predictions while capturing SO(3)-equivariance under rotational transformations, and achieves state-of-the-art performance in Hamiltonian prediction on six benchmark databases.

physics.comp-ph↗