3 citations · 6 across the 7 of their papers we have counts for
8 papers
L2G-SR: Learning to Guide LLM-based Symbolic Regression
Letian Huang, Xiangyu Wang, Haowei Lin +1
Symbolic regression searches for an equation that explains observed data. Numerical error is commonly used to guide the search, but it alone can favor an accurate surrogate over a…
Test-Time Scaling for Scientific Equation Discovery
Haowei Lin, Hubert Lim, Xiangyu Wang +2
Test-time scaling (TTS) improves language model reasoning by allocating additional test-time compute, but prior work mainly studies closed-ended tasks such as math and coding. We s…
Structured Scaling of AI Discovery Across Diverse Scientific Domains
Haotian Ye, Haowei Lin, Jingyi Tang +30
Scientific discovery often requires many cycles of proposing, testing, and refining candidate solutions. Language models can increasingly participate in these loops, but simply gen…
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…
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.…
Solving the Hubbard model with Neural Quantum States
Yuntian Gu, Wenrui Li, Heng Lin +9
The rapid development of neural quantum states (NQS) has established it as a promising framework for studying quantum many-body systems. In this work, by leveraging the cutting-edg…
Representation of General Continuous Anti-symmetric Function
Haotian Ye, Ruichen Li, Yuntian Gu +3
In quantum mechanics, the wave function of fermion systems such as many-body electron systems are anti-symmetric (AS) and continuous, and it is crucial yet challenging to find an a…
DOF: Accelerating High-order Differential Operators with Forward Propagation
Ruichen Li, Chuwei Wang, Haotian Ye +2
Solving partial differential equations (PDEs) efficiently is essential for analyzing complex physical systems. Recent advancements in leveraging deep learning for solving PDE have…