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Equivariant message passing for the prediction of tensorial properties and molecular spectra
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use PaiNN rather than distance-based or angular invariant message passing | QM9 dipole magnitude prediction · QM9 | MAE (D): 0.030 -> 0.012 | Helped |
| Use PaiNN rather than the strongest listed baseline | QM9 electronic spatial extent · QM9 | MAE (a₀²): 0.073 -> 0.066 | Helped |
| Use PaiNN rather than the strongest listed baseline | QM9 U₀ prediction · QM9 | MAE (meV): 6.32 -> 5.85 | Helped |
| Use PaiNN rather than the strongest listed baseline | QM9 HOMO-LUMO gap prediction · QM9 | MAE (meV): 32.6 -> 45.7 | Hurt |
| Use PaiNN rather than the strongest listed baseline | QM9 isotropic polarizability prediction · QM9 | MAE (a₀³): 0.044 -> 0.045 | No clear effect |
| Add scalar products of vector features to the update (ablation) | PaiNN · MD17 aspirin | energy MAE (kcal/mol): 0.173 -> 0.159 | Helped |
| Add scalar products of vector features to the update (ablation) | PaiNN · MD17 aspirin | force MAE (kcal/mol/Å): 0.420 -> 0.371 | Helped |
| Add vector-feature convolution to propagate directional information (ablation) | PaiNN · MD17 aspirin | energy MAE (kcal/mol): 0.183 -> 0.159 | Helped |
| Add vector-feature convolution to propagate directional information (ablation) | PaiNN · MD17 aspirin | force MAE (kcal/mol/Å): 0.441 -> 0.371 | Helped |
| Add vector features and their interactions (ablation) | PaiNN · MD17 aspirin | force MAE (kcal/mol/Å): 1.194 -> 0.371 | Helped |
| Predict dipoles using local vector features as well as latent charges (ablation) | PaiNN · Molecular dipoles | MAE (D): 0.020 -> 0.009 | Helped |
| Train PaiNN on forces only rather than energies and forces | PaiNN · MD17 aspirin | force MAE (kcal/mol/Å): 0.371 -> 0.338 | Helped |
Closest papers by meaning
- Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing (NeurIPS 2024 · similarity 0.92)
- Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity (ICML 2025 · similarity 0.90)
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- ELoRA: Low-Rank Adaptation for Equivariant GNNs (ICML 2025 · similarity 0.88)
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