Schnet is a continuous-filter convolutional neural network designed for modeling quantum interactions, which respects key quantum chemical constraints. It introduces continuous-filter convolutions to accommodate unevenly spaced atomic data and demonstrates state-of-the-art performance on multiple benchmarks, including qm9 and md17, while establishing iso17 for chemical and conformational variations. This work aims to enhance machine learning applications in quantum chemistry and materials science.

1. SchNet: A continuous-filter
convolutional neural
network for modeling quantum
interactions
Jin-Woo Jeong
Network Science Lab
Dept. of Mathematics
The Catholic University of Korea
E-mail: zeus0208b@catholic.ac.kr
Schütt, Kristof, et al. "Schnet: A continuous-filter convolutional neural network
for modeling quantum interactions." Advances in neural information
processing systems 30 (2017).

2. 2
 Introduction
 Continuous-filter convolutions
 SchNet
 Experiments and results
 Conclusions
Q/A

3. 3
Introduction
Introduction
• In recent years, there have been increased efforts to use machine learning for the accelerated discovery
of molecules and materials with desired properties.
• However, these methods are only applied to stable systems in so-called equilibrium.
• Data sets such as the established QM9 benchmark contain only equilibrium molecules.
• Predicting stable atom arrangements is in itself an important challenge in quantum chemistry and
material science.

4. 4
Introduction
Contributions
• Propose continuous-filter .
• Propose : a neural network specifically designed to respect essential quantum chemical constraints.
• Present a new, challenging benchmark – ISO17 – including both chemical and conformational changes.

5. 5
Continuous-filter convolutions
Continuous-filter convolutions
• Normal convolutional layers are not sufficient for unevenly spaced inputs such as the atom positions of a
molecule
• They propose to use continuous filters that are able to handle unevenly spaced data, in particular, atoms at
arbitrary positions.

6. 6
Continuous-filter convolutions
Continuous-filter convolutions
• Given the feature representations of n objects with at locations with the continuous-filter convolutional
layer requires a filter-generating function

7. 7
SchNet
Architecture
• SchNet is designed to learn a representation for the prediction of molecular energies and atomic forces.
• : non-linear

8. 8
SchNet
Architecture

9. 9
SchNet
Training with energies and forces
• the interatomic forces are related to the molecular energy, so that we can obtain an energy-conserving
force model by differentiating the energy model w.r.t. the atom positions
• Loss fuction:

10. 10
Experiments and results
Datasets
• QM9 – chemical degrees of freedom
• Task: Predicting chemical properties
• Contains only equilibrium molecules
• Diverse chemical property data
• Molecule size: up to 9 heavy atoms (C, O, N, F included)
• Includes energy, dipole moment, HOMO/LUMO, etc.
• MD17 – conformational degrees of freedom
• Task: Molecular dynamics
• Predicting conformational changes of single molecules
• Includes molecular trajectories
• Provides energy and force data
• Based on small organic molecules
• ISO17 – chemical and conformational degrees of freedom
• Task: Predicting chemical + conformational variations
• Includes isomers ()
• Data on both chemical and structural variations
• 645,000 labeled examples

11. 11
Experiments and results
Results

12. 12
Experiments and results
Results

13. 13
Experiments and results
Results

14. 14
Conclusion
Conclusion
• SchNet introduces continuous-filter convolutional layers to model quantum interactions in molecules,
respecting fundamental quantum-chemical principles like energy conservation and rotational invariance.
• It achieves state-of-the-art performance on QM9 and MD17 datasets and establishes ISO17 as a
challenging benchmark for modeling both chemical and conformational variations, paving the way for
advancements in machine learning-driven quantum chemistry.

15. 15
Q & A
Q / A