repoGitHubTrust 82 · PrimaryPublished 2d agoLive · 2d ago
atomistic-machine-learning/schnetpack
SchNetPack - Deep Neural Networks for Atomistic Systems
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 54%Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications →
- PossiblePossibly related (embedding) · 49%An Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural Networks →
- PossiblePossibly related (embedding) · 49%Active rejection enables reliable generalization of universal machine-learning interatomic potentials →
- PossiblePossibly related (embedding) · 48%Hamiltonian Neural Networks from a Differential Geometry Perspective [D] →
- PossiblePossibly related (embedding) · 48%One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective →
Implements
paperQ-GAIN: A Python Package for Machine Learning and Physically Informed Analysis ApplicationspaperAn Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural NetworkspaperActive rejection enables reliable generalization of universal machine-learning interatomic potentialspaperOne More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective
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Related across the graph
paperOne More Time: Revisiting Neural Quantum States from a Reinforcement Learning PerspectivepaperAn Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural NetworkspaperActive rejection enables reliable generalization of universal machine-learning interatomic potentialsnewsHamiltonian Neural Networks from a Differential Geometry Perspective [D]paperQ-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications