arXiv subjects

Weizhong Fu

Publications and source records attributed to Weizhong Fu.

11 recordsLinked to original sources

arXiv2026

Empowering Neural Network-based Quantum Monte Carlo with Local Pseudopotentials

Neural Network-based Quantum Monte Carlo (NNQMC), an emerging method for solving many-body quantum systems with high accuracy, has been mainly applied to small systems due to demanding computation requirements. In this work, we introduce a framework based on local pseudopotentials to break through such limitation, improving the computational efficiency and scalability of NNQMC. The incorporation of local pseudopotentials reduces the number of electrons treated in neural network and also achieves better relative energy accuracy than all electron NNQMC calculations for complex systems. This counterintuitive outcome is made possible by the distinctive characteristics inherent to NNQMC. Notably, by avoiding costly integration terms, this approach is also substantially more efficient than its widely used semilocal counterparts. Our approach enables the reliable treatment of large and challenging systems, such as the $\text{Fe}_4 \text{S}_4 (\text{SCH}_3)_4$ iron-sulfur cluster. Overall, our findings demonstrate that the synergy between NNQMC and local pseudopotentials substantially expands the scope of accurate ab initio calculations.

physics.chem-ph↗

arXiv2026

Towards stable and accurate electron dynamics via neural network based time-dependent variational Monte Carlo

Real-time dynamics of interacting electrons lies at the interface between quantum mechanics and non-equilibrium physics, governing the microscopic origin of ultrafast phenomena of molecules and nano-materials. Though neural network variational Monte Carlo has achieved unprecedented accuracy for stationary state calculations, its extension to real-time evolution remains challenging. In this work, we introduce the neural basis time-dependent variational Monte Carlo framework, which achieves stable and highly accurate simulations of electron dynamics. By constraining the time evolution to a compact, customized manifold spanned by the neural basis, we effectively bypass instability issues and achieve long-term stable evolution. Moreover, we demonstrate that this framework yields benchmark-quality accuracy in simulating the laser-driven dipole responses of the hydrogen atom and a stretched hydrogen molecule, and accurately extracts the dynamic polarizabilities of helium and beryllium atoms. Our work reveals the vast potential of neural network wavefunctions for accurately describing real-time electron dynamics and establishes a promising new route for first-principles simulations of complex, time-dependent electronic phenomena.

physics.comp-ph↗

arXiv2026

Permutation invariant multi-scale full quantum neural network wavefunction

Solving the intricate quantum behavior of interacting particles is key to unlocking the mysteries of condensed matter, but capturing their complex correlations across different scales remains a monumental challenge. We introduce a neural network framework that overcomes this barrier by modeling the full quantum wavefunction of a system, including electrons, nuclei and muons, directly capturing the full quantum effects beyond the Born-Oppenheimer approximation. The neural network approximates joint wavefunction of different interacting particles with a rigorous handling of permutation invariance, enabling simultaneous treatment of nuclear quantum effects and electron-nucleus-muon couplings without explicit excited states. Validated on molecular systems, this approach offers a computationally feasible way to model full quantum phenomena in complex many-body systems, establishing a direct connection between fundamental particle properties and emergent material behavior.

physics.chem-ph↗

arXiv2025

Stochastic Representation of Time-Evolving Neural Network-based Wavefunctions

Solving the time-dependent Schrödinger equation (TDSE) is pivotal for modeling non-adiabatic electron dynamics, a key process in ultrafast spectroscopy and laser-matter interactions. However, exact solutions to the TDSE remain computationally prohibitive for most realistic systems, as the Hilbert space expands exponentially with dimensionality. In this work, we propose an approach integrating the stochastic representation framework with a neural network wavefunction ansatz, a flexible model capable of approximating time-evolving quantum wavefunctions. We first validate the method on one-dimensional single-electron systems, focusing on ionization dynamics under intense laser fields, a critical process in attosecond physics. Our results demonstrate that the approach accurately reproduces key features of quantum evolution, including the energy and dipole evolution during ionization. We further show the feasibility of extending this approach to three-dimensional systems. Due to the increased complexity of real-time simulations in higher dimensions, these results remain at an early stage and highlight the need for more advanced stabilization strategies.

physics.chem-ph↗

arXiv2025

A particle view of many-body electronic structure with neural network wavefunction

In the study of electronic structure, the wavefunction view dominates the current research landscape and forms the theoretical foundation of modern quantum mechanics. In contrast, Valence Bond (VB) theory represents chemical bonds as shared electron-pairs and can provide an intuitive, particle-based insight into chemical bonding. In this work, using a newly developed Periodic Dynamic Voronoi Metropolis Sampling (PDVMS) method, we project classical many-body electronic configurations from the neural network wavefunction, and apply VB theory to construct a complementary particle-view paradigm. The powerful neural network wavefunction can achieve near-exact ab initio solutions for the ground state of both molecular and solid systems. It allows us to definitively characterize the ground state of benzene by reassessing the competition between its two VB structures. Extending PDVMS to solids for the first time, we also predict a spin-staggered VB structure in graphene, which explains emergent magnetic properties in graphene-based nanostructures. Furthermore, this particle view provides insight into the optimization process of the neural network wavefunction itself. Our work thus introduces a novel framework for analyzing many-body electronic structure in molecules and solids, opening new avenues for investigating this complex problem and its associated exotic phenomena.

physics.chem-ph↗

arXiv2025

Neural Scaling Laws Surpass Chemical Accuracy for the Many-Electron Schrödinger Equation

We demonstrate, for the first time, that neural scaling laws can deliver near-exact solutions to the many-electron Schrödinger equation across a broad range of realistic molecules. This progress is enabled by the Lookahead Variational Algorithm (LAVA), an effective optimization scheme that systematically translates increased model size and computational resources into greatly improved energy accuracy for neural network wavefunctions. Across all tested cases, including benzene, the absolute energy error exhibits a systematic power-law decay with respect to model capacity and computation resources. The resulting energies not only surpass the 1 kcal/mol "chemical-accuracy" threshold but also achieve 1 kJ/mol subchemical accuracy. Beyond energies, the scaled-up neural network also yields better wavefunctions with improved physical symmetries, alongside accurate electron densities, dipole moments, and other important properties. Our approach offers a promising way forward to addressing many long-standing challenges in quantum chemistry. For instance, we improve energetic properties for systems such as the potential energy curve of nitrogen dimer as dissociation is approached and the cyclobutadiene automerization reaction barrier, producing definitive benchmarks, particularly in regimes where experimental data are sparse or highly uncertain. We also shed light on the decades-old puzzle of the cyclic ozone stability with highly accurate calculations for the cyclic-to-open ozone barrier. These results provide near-exact reference calculations with unprecedented accuracy, universal reliability and practical applicability, establishing a foundation for AI-driven quantum chemistry.

physics.chem-ph↗

arXiv2023

Penalty and auxiliary wave function methods for electronic Excitation in neural network variational Monte Carlo

This study explores the application of neural network variational Monte Carlo (NN-VMC) for the computation of low-lying excited states in molecular systems. Our focus lies on the implementation and evaluation of two distinct methodologies, the penalty method and a novel modification of the auxiliary wave function (AW) method, within the framework of the FermiNet-based NN-VMC package. Importantly, this specific application has not been previously reported.Our investigation advocates for the efficacy of the modified AW method, emphasizing its superior robustness when compared to the penalty method. This methodological advancement introduces a valuable tool for the scientific community, offering a distinctive approach to target low-lying excited states. We anticipate that the modified AW method will garner interest within the research community, serving as a complementary and robust alternative to existing techniques. Moreover, this contribution enriches the ongoing development of various neural network ansatz, further expanding the toolkit available for the accurate exploration of excited states in molecular systems.

physics.comp-ph↗

arXiv2023

Variance extrapolation method for neural-network variational Monte Carlo

Constructing more expressive ansatz has been a primary focus for quantum Monte Carlo, aimed at more accurate \textit{ab initio} calculations. However, with more powerful ansatz, e.g. various recent developed models based on neural-network architectures, the training becomes more difficult and expensive, which may have a counterproductive effect on the accuracy of calculation. In this work, we propose to make use of the training data to perform variance extrapolation when using neural-network ansatz in variational Monte Carlo. We show that this approach can speed up the convergence and surpass the ansatz limitation to obtain an improved estimation of the energy. Moreover, variance extrapolation greatly enhances the error cancellation capability, resulting in significantly improved relative energy outcomes, which are the keys to chemistry and physics problems.

physics.comp-ph↗

arXiv2023

Towards the ground state of molecules via diffusion Monte Carlo on neural networks

Diffusion Monte Carlo (DMC) based on fixed-node approximation has enjoyed significant developments in the past decades and become one of the go-to methods when accurate ground state energy of molecules and materials is needed. The remaining bottleneck is the limitations of the inaccurate nodal structure, prohibiting more challenging electron correlation problems to be tackled with DMC. In this work, we apply the neural-network based trial wavefunction in fixed-node DMC, which allows accurate calculation of a broad range of atomic and molecular systems of different electronic characteristics. Our method is superior in both accuracy and efficiency compared to state-of-the-art neural network methods using variational Monte Carlo. Overall, this computational framework provides a new benchmark for accurate solution of correlated electronic wavefunction and also shed light on the chemical understanding of molecules.

physics.chem-ph↗

arXiv2022

Interatomic force from neural network based variational quantum Monte Carlo

Accurate ab initio calculations are of fundamental importance in physics, chemistry, biology, and materials science, which have witnessed rapid development in the last couple of years with the help of machine learning computational techniques such as neural networks. Most of the recent efforts applying neural networks to ab initio calculation have been focusing on the energy of the system. In this study, we take a step forward and look at the interatomic force obtained with neural network wavefunction methods by implementing and testing several commonly used force estimators in variational quantum Monte Carlo (VMC). Our results show that neural network ansatz can improve the calculation of interatomic force upon traditional VMC. The relation between the force error and the quality of neural network, the contribution of different force terms, and the computational cost of each term are also discussed to provide guidelines for future applications. Our work demonstrates that it is promising to apply neural network wavefunction methods in simulating structures/dynamics of molecules/materials and provide training data for developing accurate force fields.

physics.chem-ph↗

arXiv2021

Deep vacancy induced low-density fluxional interfacial water

Interfacial water on transition metal oxides such as TiO$_2$ has been widely studied because of their structural complexity and scientific relevance in e.g. photocatalysis and ice growth. Using ab initio molecular dynamics, we find that interfacial water on anatase (101) surface features an unconventional fluxional structure with reduced contact layer density. The density reduction and flexibility of interfacial water are induced by oxygen vacancy defects deeply located in the subsurface. Our study proposes a fresh perspective of the anatase/water interface, raising the importance of non-trivial long-range effects caused by deep defects. These often-neglected effects highlight the necessity and challenges of the state-of-the-art simulation and experimental probing of solid liquid interfaces.

cond-mat.mtrl-sci↗