repoGitHubTrust 82 · PrimaryPublished 1mo agoLive · 1mo ago
deepmodeling/DeePTB
DeePTB: A deep learning package for tight-binding Hamiltonian with ab initio accuracy.
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 52%Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark →
- PossiblePossibly related (embedding) · 49%Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications →
- PossiblePossibly related (embedding) · 48%Bridging the NISQ and Fault-Tolerant Regimes: Generative-ML-Assisted Quantum Selected CI for Molecular Simulations →
- PossiblePossibly related (embedding) · 47%Bridging Ab Initio Symmetries and Global Nuclear Masses with Interpretable Neural Networks →
- PossiblePossibly related (embedding) · 47%Helix-7B →
- PossiblePossibly related (embedding) · 54%Active rejection enables reliable generalization of universal machine-learning interatomic potentials →
Implements
paperBeyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) BenchmarkpaperQ-GAIN: A Python Package for Machine Learning and Physically Informed Analysis ApplicationspaperBridging the NISQ and Fault-Tolerant Regimes: Generative-ML-Assisted Quantum Selected CI for Molecular SimulationspaperBridging Ab Initio Symmetries and Global Nuclear Masses with Interpretable Neural Networks
Related to
Implements (incoming)
Related across the graph
paperBridging the NISQ and Fault-Tolerant Regimes: Generative-ML-Assisted Quantum Selected CI for Molecular SimulationspaperBeyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) BenchmarkpaperActive rejection enables reliable generalization of universal machine-learning interatomic potentialspaperBridging Ab Initio Symmetries and Global Nuclear Masses with Interpretable Neural NetworkspaperQ-GAIN: A Python Package for Machine Learning and Physically Informed Analysis ApplicationsmodelHelix-7B