1 citations · 1 across the 9 of their papers we have counts for
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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.…
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…