Reaction virtual screening and discovery are fundamental challenges in chemistry and material science, where traditional graph neural networks (GNNs) struggle to model multi-reactant interactions. In this work, we propose ChemHGNN, a hypergraph neural network (HGNN) framework that effectively captures high-order relati...
Xiaobao Huang, Yi-Hong Ma, Anjali Gurajapu et al.· Proceedings of the 32nd ACM...· 1 citation
Institutions use commercial AI detectors for academic integrity, yet detectors cannot distinguish AI editing from full LLM drafts and may treat both as misconduct. In a controlled study of published English abstracts (four domains; 2013 to 2015 vs. 2023 to 2025), we quantify this policy failure under proxy human/AI lab...
Jonathan A. Karr, Grigorii Khvatskii, Hua Ting et al.· 0 citations
Reaction virtual screening and discovery are fundamental challenges in chemistry and material science, where traditional graph neural networks (GNNs) struggle to model multi-reactant interactions. In this work, we propose ChemHGNN, a hypergraph neural network (HGNN) framework that effectively captures high-order relati...
Xiaobao Huang, Yihong Ma, Anjali Gurajapu et al.· Proceedings of the 32nd ACM...· 0 citations
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