Research map / Graph learning / GNN architectures
On the equivalence of molecular graph convolution and molecular wave function with poor basis set
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace GCN with QDF (physics-based LCAO model) | GCN · QM9under14atoms | MAE (kcal/mol): 1.58 -> 1.21 | Helped |
| QDF versus SchNet in interpolation | QDF · QM9under14atoms | MAE (kcal/mol): 1.23 -> 1.21 | No clear effect |
| QDF versus SchNet in model size | QDF · QM9under14atoms | model size (parameters): 1,676,133 -> 495,262 | Helped |
| QDF versus GCN in model size | QDF · QM9under14atoms | model size (parameters): 483,631 -> 495,262 | No clear effect |
| QDF versus GCN on extrapolation to molecules with more than 15 atoms | QDF · QM9over15atoms (energy, ZPVE, enthalpy) | MAE | Helped |
| QDF versus GCN on interpolation | QDF · QM9under14atoms (3 energy properties) | MAE | No clear effect |
Closest papers by meaning
- Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective (AAAI 2019 · similarity 0.90)
- Learning the Electronic Hamiltonian of Large Atomic Structures (ICML 2025 · similarity 0.89)
- Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems (ICLR 2025 · similarity 0.88)
- $\nabla^2$DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials (NeurIPS 2024 · similarity 0.87)
- High-order Equivariant Flow Matching for Density Functional Theory Hamiltonian Prediction (NeurIPS 2025 · similarity 0.87)
- The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains (NeurIPS 2024 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.