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Equivariant message passing for the prediction of tensorial properties and molecular spectra

ICML 2021 · Graph learning › GNN architectures · approach: Equivariant networks for molecules · 253 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use PaiNN rather than distance-based or angular invariant message passingQM9 dipole magnitude prediction · QM9MAE (D): 0.030 -> 0.012Helped
Use PaiNN rather than the strongest listed baselineQM9 electronic spatial extent · QM9MAE (a₀²): 0.073 -> 0.066Helped
Use PaiNN rather than the strongest listed baselineQM9 U₀ prediction · QM9MAE (meV): 6.32 -> 5.85Helped
Use PaiNN rather than the strongest listed baselineQM9 HOMO-LUMO gap prediction · QM9MAE (meV): 32.6 -> 45.7Hurt
Use PaiNN rather than the strongest listed baselineQM9 isotropic polarizability prediction · QM9MAE (a₀³): 0.044 -> 0.045No clear effect
Add scalar products of vector features to the update (ablation)PaiNN · MD17 aspirinenergy MAE (kcal/mol): 0.173 -> 0.159Helped
Add scalar products of vector features to the update (ablation)PaiNN · MD17 aspirinforce MAE (kcal/mol/Å): 0.420 -> 0.371Helped
Add vector-feature convolution to propagate directional information (ablation)PaiNN · MD17 aspirinenergy MAE (kcal/mol): 0.183 -> 0.159Helped
Add vector-feature convolution to propagate directional information (ablation)PaiNN · MD17 aspirinforce MAE (kcal/mol/Å): 0.441 -> 0.371Helped
Add vector features and their interactions (ablation)PaiNN · MD17 aspirinforce MAE (kcal/mol/Å): 1.194 -> 0.371Helped
Predict dipoles using local vector features as well as latent charges (ablation)PaiNN · Molecular dipolesMAE (D): 0.020 -> 0.009Helped
Train PaiNN on forces only rather than energies and forcesPaiNN · MD17 aspirinforce MAE (kcal/mol/Å): 0.371 -> 0.338Helped

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