63 citations · 114 across the 20 of their papers we have counts for
21 papers
Towards stable and accurate electron dynamics via neural network based time-dependent variational Monte Carlo
Weizhong Fu, Zhe Li, Yubing Qian +3
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…
Topological invariant of periodic many body wavefunction from charge pumping simulation
Haoxiang Chen, Yubing Qian, Weiluo Ren +2
Many-body topological quantum states host exotic quantum phenomena and lie at the forefront of developing next-generation quantum technologies. Recently emerged neural network wave…
Dataset Distillation for Machine Learning Force Field in Phase Transition Regime
Ruiyang Chen, Qingyuan Zhang, Ji Chen
Machine learning force field (MLFF) has emerged as a powerful data-driven tool for atomistic simulations, enabling large-scale and complex atomic systems to be simulated with accur…
Permutation invariant multi-scale full quantum neural network wavefunction
Pengzhen Cai, Yubing Qian, Li Deng +8
Solving the intricate quantum behavior of interacting particles is key to unlocking the mysteries of condensed matter, but capturing their complex correlations across different sca…
Spin-Adapted Neural Network Wavefunctions in Real Space
Ruichen Li, Yuzhi Liu, Du Jiang +7
Spin plays a fundamental role in understanding electronic structure, yet many real-space wavefunction methods fail to adequately consider it. We introduce the Spin-Adapted Antisymm…
A particle view of many-body electronic structure with neural network wavefunction
Zichen Wang, Weizhong Fu, Zhe Li +2
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,…
Neural Scaling Laws Surpass Chemical Accuracy for the Many-Electron Schrödinger Equation
Du Jiang, Xuelan Wen, Yixiao Chen +8
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.…
Empowering Neural Network-based Quantum Monte Carlo with Local Pseudopotentials
Weizhong Fu, Ryunosuke Fujimaru, Ruichen Li +9
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 deman…
Deep Learning Sheds Light on Integer and Fractional Topological Insulators
Xiang Li, Yixiao Chen, Bohao Li +4
Electronic topological phases of matter, characterized by robust boundary states derived from topologically nontrivial bulk states, are pivotal for next-generation electronic devic…
Taming Landau level mixing in fractional quantum Hall states with deep learning
Yubing Qian, Tongzhou Zhao, Jianxiao Zhang +3
Strong correlation brings a rich array of emergent phenomena, as well as a daunting challenge to theoretical physics study. In condensed matter physics, the fractional quantum Hall…
Probing quantum critical phase from neural network wavefunction
Haoxiang Chen, Weiluo Ren, Xiang Li +1
One-dimensional (1D) systems and models provide a versatile platform for emergent phenomena induced by strong electron correlation. In this work, we extend the newly developed real…
Deep learning quantum Monte Carlo for solids
Yubing Qian, Xiang Li, Zhe Li +2
Deep learning has deeply changed the paradigms of many research fields. At the heart of chemical and physical sciences is the accurate ab initio calculation of many-body wavefuncti…
Emergent Wigner phases in moiré superlattice from deep learning
Xiang Li, Yubing Qian, Weiluo Ren +2
Moiré superlattice designed in stacked van der Waals material provides a dynamic platform for hosting exotic and emergent condensed matter phenomena. However, the relevance of stro…
Symmetry enforced solution of the many-body Schrödinger equation with deep neural network
Zhe Li, Zixiang Lu, Ruichen Li +5
The integration of deep neural networks with the Variational Monte Carlo (VMC) method has marked a significant advancement in solving the Schrödinger equation. In this work, we enf…
Electric Polarization from Many-Body Neural Network Ansatz
Xiang Li, Yubing Qian, Ji Chen
Ab initio calculation of dielectric response with high-accuracy electronic structure methods is a long-standing problem, for which mean-field approaches are widely used and electro…
Variance extrapolation method for neural-network variational Monte Carlo
Weizhong Fu, Weiluo Ren, Ji Chen
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.…
Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo
Ruichen Li, Haotian Ye, Du Jiang +8
Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry. However, the high computational cost of exis…
Interatomic force from neural network based variational quantum Monte Carlo
Yubing Qian, Weizhong Fu, Weiluo Ren +1
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 ye…
Towards the ground state of molecules via diffusion Monte Carlo on neural networks
Weiluo Ren, Weizhong Fu, Xiaojie Wu +1
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 stat…
Fermionic Neural Network with Effective Core Potential
Xiang Li, Cunwei Fan, Weiluo Ren +1
Deep learning techniques have opened a new venue for electronic structure theory in recent years. In contrast to traditional methods, deep neural networks provide much more express…
Deep vacancy induced low-density fluxional interfacial water
Keyang Liu, Jianqing Guo, Weizhong Fu +1
Interfacial water on transition metal oxides such as TiO has been widely studied because of their structural complexity and scientific relevance in e.g. photocatalysis and ice…