5 citations · 6 across the 4 of their papers we have counts for
4 papers
Transferable Machine Learning of Electronic Hamiltonians with Superposition-of-Atomic-Potentials Features
Chaoqun Zhang, Christian Venturella, Enzhi Chen +1
Machine learning (ML) of electronic Hamiltonians offers a unified route to electronic wave functions and physical observables. We introduce a Hamiltonian learning framework built o…
A Large-Scale Dataset for Molecular Structure-Language Description via a Rule-Regularized Method
Feiyang Cai, Guijuan He, Yi Hu +7
Molecular function is largely determined by structure. Accurately aligning molecular structure with natural language is therefore essential for enabling large language models (LLMs…
MolLangBench: A Comprehensive Benchmark for Language-Prompted Molecular Structure Recognition, Editing, and Generation
Feiyang Cai, Jiahui Bai, Tao Tang +7
Precise recognition, editing, and generation of molecules are essential prerequisites for both chemists and AI systems tackling various chemical tasks. We present MolLangBench, a c…
ChemFM as a Scaling Law Guided Foundation Model Pre-trained on Informative Chemicals
Feiyang Cai, Katelin Zacour, Tianyu Zhu +6
Traditional AI methods often rely on task-specific model designs and training, which constrain both the scalability of model size and generalization across different tasks. Here, w…