Gap report
SchNet: A Continuous-Filter Convolutional Neural Network for Modeling Quantum Interactions
Schutt K et al. · NeurIPS, 2017
The architecture is well known, but production translation still depends on several omitted training details.
Specified
Continuous-filter graph convolution is the core approach.
Quantum interaction targets are clearly scoped.
Partial
Architecture depth and hidden size are described without every training constant.
Benchmark setup is clear at paper level, not fully operationalized.
Missing
Exact seed policy.
Library version pinning.
Detailed preprocessing for every dataset variant.
Template fit
GNN regression
Confidence: Medium. Reported gaps: 6.
Next step
Use this example gap report as a review aid, then check the matching template coverage before relying on generated defaults for a real paper translation.