papers

Publications (31)

physics.comp-ph2020

The dynamic parallel distribution algorithm for hybrid density-functional calculations in HONPAS package

Honghui Shang, Lei Xu, Baodong Wu +3

This work presents a dynamic parallel distribution scheme for the Hartree-Fock exchange~(HFX) calculations based on the real-space NAO2GTO framework. The most time-consuming electr…

physics.chem-ph2021

Localized resolution of identity approach to the analytical gradients of random-phase approximation ground-state energy: algorithm and benchmarks

Muhammad N. Tahir, Tong Zhu, Honghui Shang +3

We develop and implement a formalism which enables calculating the analytical gradients of particle-hole random-phase approximation (RPA) ground-state energy with respect to the at…

quant-ph2022

QChemistry: A quantum computation platform for quantum chemistry

Yi Fan, Jie Liu, Xiongzhi Zeng +4

Quantum computer provides new opportunities for quantum chemistry. In this article, we present a versatile, extensible, and efficient software package, named QChemistry, for de…

quant-ph2022

Large-Scale Simulation of Quantum Computational Chemistry on a New Sunway Supercomputer

Honghui Shang, Li Shen, Yi Fan +11

Quantum computational chemistry (QCC) is the use of quantum computers to solve problems in computational quantum chemistry. We develop a high performance variational quantum eigens…

cond-mat.mtrl-sci2026

Implementation of the hybrid exchange-correlation functionals in the SIESTA code

Yann Pouillon, Bill Clintone Oyomo, James Sifuna +6

We present an efficient and accurate implementation of hybrid exchange-correlation (XC) functionals in the SIESTA code, enabling large-scale simulations based on Hartree-Fock-type…

cs.AI2025

SwarmThinkers: Learning Physically Consistent Atomic KMC Transitions at Scale

Qi Li, Kun Li, Haozhi Han +6

Can a scientific simulation system be physically consistent, interpretable by design, and scalable across regimes--all at once? Despite decades of progress, this trifecta remains e…

physics.comp-ph2023

TensorMD: Scalable Tensor-Diagram based Machine Learning Interatomic Potential on Heterogeneous Many-Core Processors

Xin Chen, Yucheng Ouyang, Zhenchuan Chen +7

Molecular dynamics simulations have emerged as a potent tool for investigating the physical properties and kinetic behaviors of materials at the atomic scale, particularly in extre…

cond-mat.mtrl-sci2017

Lattice Dynamics Calculations based on Density-functional Perturbation Theory in Real Space

Honghui Shang, Christian Carbogno, Patrick Rinke +1

A real-space formalism for density-functional perturbation theory (DFPT) is derived and applied for the computation of harmonic vibrational properties in molecules and solids. The…

physics.comp-ph2024

Advancing Nonadiabatic Molecular Dynamics Simulations for Solids: Achieving Supreme Accuracy and Efficiency with Machine Learning

Changwei Zhang, Yang Zhong, Zhi-Guo Tao +7

Non-adiabatic molecular dynamics (NAMD) simulations have become an indispensable tool for investigating excited-state dynamics in solids. In this work, we propose a general framewo…

quant-ph2023

NNQS-Transformer: an Efficient and Scalable Neural Network Quantum States Approach for Ab initio Quantum Chemistry

Yangjun Wu, Chu Guo, Yi Fan +2

Neural network quantum state (NNQS) has emerged as a promising candidate for quantum many-body problems, but its practical applications are often hindered by the high cost of sampl…

quant-ph2022

Divide-and-conquer variational quantum algorithms for large-scale electronic structure simulations

Huan Ma, Yi Fan, Jie Liu +3

Exploring the potential application of quantum computers in material design and drug discovery has attracted a lot of interest in the age of quantum computing. However, the quantum…

quant-ph2023

A real neural network state for quantum chemistry

Yangjun Wu, Xiansong Xu, Dario Poletti +3

The restricted Boltzmann machine (RBM) has been successfully applied to solve the many-electron Schrdinger equation. In this work we propose a single-layer fully c…

physics.chem-ph2025

Analytical gradients of random-phase approximation plus corrections from renormalized single excitations

Muhammad N. Tahir, Honghui Shang, Xinguo Ren

The random-phase approximation (RPA) formulated within the adiabatic connection fluctuation-dissipation framework is a powerful approach to compute the ground-state energies and pr…

quant-ph2024

Rapidly Achieving Chemical Accuracy with Quantum Computing Enforced Language Model

Honghui Shang, Xiongzhi Zeng, Ming Gong +11

Finding accurate ground state energy of a many-body system has been a major challenge in quantum chemistry. The integration of classic and quantum computers has shed new light on r…

cond-mat.mtrl-sci2018

All-Electron, Real-Space Perturbation Theory for Homogeneous Electric Fields: Theory, Implementation, and Application within DFT

Honghui Shang, Nathaniel Raimbault, Patrick Rinke +3

Within density-functional theory, perturbation theory~(PT) is the state-of-the-art formalism for assessing the response to homogeneous electric fields and the associated material p…

quant-ph2023

Differentiable matrix product states for simulating variational quantum computational chemistry

Chu Guo, Yi Fan, Zhiqian Xu +1

Quantum Computing is believed to be the ultimate solution for quantum chemistry problems. Before the advent of large-scale, fully fault-tolerant quantum computers, the variational…

physics.comp-ph2020

The static parallel distribution algorithms for hybrid density-functional calculations in HONPAS package

Xinming Qin, Honghui Shang, Lei Xu +4

Hybrid density-functional calculation is one of the most commonly adopted electronic structure theory used in computational chemistry and materials science because of its balance b…

cond-mat.mtrl-sci2025

Density-Functional Perturbation Theory with Numeric Atom-Centered Orbitals

Connor L. Box, Reinhard J. Maurer, Honghui Shang +4

This paper represents one contribution to a larger Roadmap article reviewing the current status of the FHI-aims code. In this contribution, the implementation of density-functional…

quant-ph2023

Towards practical and massively parallel quantum computing emulation for quantum chemistry

Honghui Shang, Yi Fan, Li Shen +5

Quantum computing is moving beyond its early stage and seeking for commercial applications in chemical and biomedical sciences. In the current noisy intermediate-scale quantum comp…

physics.comp-ph2020

The Moving-Grid Effect in the Harmonic Vibrational Frequency Calculations with Numeric Atom-Centered Orbitals

Honghui Shang, Jinlong Yang

When using atom-centered integration grids, the portion of the grid that belongs to a certain atom also moves when this atom is displaced. In the paper, we investigate the moving-g…

quant-ph2024

Solving Schrödinger Equation with a Language Model

Honghui Shang, Chu Guo, Yangjun Wu +2

Accurately solving the Schrödinger equation for intricate systems remains a prominent challenge in physical sciences. A paradigm-shifting approach to address this challenge involv…

cond-mat.mtrl-sci2020

Electron-phonon coupling in d-electron solids: A temperature dependent study of rutile TiO2 by first-principles theory and two-photon photoemission

Honghui Shang, Adam Argondizzo, Shijing Tan +5

Rutile TiO2 is a paradigmatic transition metal oxide with applications in optics, electronics, photocatalysis, etc., that are subject to pervasive electron-phonon interaction. To u…

cs.DC2026

A Fully GPU-Accelerated Framework for High-Performance Configuration Interaction Selection with Neural Network Quantum States

Daran Sun, Bowen Kan, Haoquan Long +13

AI-driven methods have demonstrated considerable success in tackling the central challenge of accurately solving the Schrödinger equation for complex many-body systems. Among neur…

quant-ph2025

Transformer-Based Neural Networks Backflow for Strongly Correlated Electronic Structure

Huan Ma, Bowen Kan, Honghui Shang +1

Solving the electronic Schrödinger equation for strongly correlated systems remains one of the grand challenges in quantum chemistry. Here we demonstrate that Transformer architec…

quant-ph2026

Transformer refined quantum sampling for strongly correlated electronic structure

Xiongzhi Zeng, Ming Gong, Bowen Kan +18

Although quantum computing offers a promising solution for strongly correlated system simulation, existing algorithms face significant bottlenecks on current noisy intermediate-sca…

physics.chem-ph2025

NNQS-AFQMC: Neural network quantum states enhanced fermionic quantum Monte Carlo

Zhi-Yu Xiao, Bowen Kan, Huan Ma +2

We introduce an efficient approach to implement neural network quantum states (NNQS) as trial wavefunctions in auxiliary-field quantum Monte Carlo (AFQMC). NNQS are a recently deve…

cond-mat.mtrl-sci2026

Roadmap on Advancements of the FHI-aims Software Package

Joseph W. Abbott, Carlos Mera Acosta, Alaa Akkoush +203

Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accurac…

cond-mat.mtrl-sci2024

Efficient structural relaxation based on the random phase approximation: Applications to the water clusters

Muhammad N. Tahir, Honghui Shang, Jia Li +1

We report an improved implementation for evaluating the analytical gradients of the random phase approximation (RPA) electron-correlation energy based on atomic orbitals and the lo…

physics.comp-ph2020

Efficient Parallel Linear Scaling Method to get the Response Density Matrix in All-Electron Real-Space Density-Functional Perturbation Theory

Honghui Shang, Wanzhen Liang, Yunquan Zhang +1

The real-space density-functional perturbation theory (DFPT) for the computations of the response properties with respect to the atomic displacement and homogeneous electric field…

cond-mat.mtrl-sci2020

The influence of high-energy local orbitals and electron-phonon interactions on the band gaps and optical spectra of hexagonal boron nitride

Tong Shen, Xiao-Wei Zhang, Honghui Shang +5

We report band diagram and optical absorption spectra of hexagonal boron nitride (-BN), focusing on unravelling how the completeness of basis set for calculat…

quant-ph2025

Clifford augmented density matrix renormalization group for \textit{ab initio} quantum chemistry

Lizhong Fu, Honghui Shang, Jinlong Yang +1

The recently proposed Clifford augmented density matrix renormalization group (CA-DMRG) method seamlessly integrates Clifford circuits with matrix product states, and takes advanta…