63 citations · 127 across the 25 of their papers we have counts for
11 papers · 1 filter
A Unified Generative Framework for Scalable Chemical Reaction Network Exploration
Zechang Sun, Chenxi Hu, Kailai Lin +6
Chemical reaction networks (CRNs) are crucial for understanding reaction mechanisms and guiding chemical synthesis, yet the computational exploration remains limited by the combina…
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 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…
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…
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…