41 citations · 131 across the 8 of their papers we have counts for
8 papers
Overcoming the Weakest-Link Effect in LLM-Driven Program Optimization via Heterogeneous Edit Recombination
Jingwen Fu, Zhen Liu, Yuhan Liu +2
Large language models (LLMs) are increasingly used to solve complex problems by searching over program space, offering a general paradigm for scientific problems that can be natura…
Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models
Zun Wang, Chang Liu, Nianlong Zou +5
In this study, we introduce a unified neural network architecture, the Deep Equilibrium Density Functional Theory Hamiltonian (DEQH) model, which incorporates Deep Equilibrium Mode…
Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction
He Zhang, Chang Liu, Zun Wang +5
Predicting the mean-field Hamiltonian matrix in density functional theory is a fundamental formulation to leverage machine learning for solving molecular science problems. Yet, its…
Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning
He Zhang, Siyuan Liu, Jiacheng You +6
Orbital-free density functional theory (OFDFT) is a quantum chemistry formulation that has a lower cost scaling than the prevailing Kohn-Sham DFT, which is increasingly desired for…
Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning
Shuxin Zheng, Jiyan He, Chang Liu +15
Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not fun…
Unified Abstract Syntax Tree Representation Learning for Cross-Language Program Classification
Kesu Wang, Meng Yan, He Zhang +1
Program classification can be regarded as a high-level abstraction of code, laying a foundation for various tasks related to source code comprehension, and has a very wide range of…
Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model
Liang He, Shizhuo Zhang, Lijun Wu +10
Understanding protein sequences is vital and urgent for biology, healthcare, and medicine. Labeling approaches are expensive yet time-consuming, while the amount of unlabeled data…
SE(3) Equivariant Graph Neural Networks with Complete Local Frames
Weitao Du, He Zhang, Yuanqi Du +4
Group equivariance (e.g. SE(3) equivariance) is a critical physical symmetry in science, from classical and quantum physics to computational biology. It enables robust and accurate…