repoGitLabTrust 82 · PrimaryPublished 1mo agoLive · yesterday
electrocatalysis-group/atomic-recipes
Implementations from the Theoretical Electrocatalysis Group at the Indian Institute of Technology Bombay. Implementations are related to machine learning for materials, descriptors of machine learning interatomic potentials and electrostatics with density functional theory calculations.
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 53%Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark →
- PossiblePossibly related (embedding) · 49%Guiding generative models to uncover diverse and novel crystals via reinforcement learning →
- PossiblePossibly related (embedding) · 48%Physics-Informed Neural Network with Transfer Learning for State Estimation in Lithium-Ion Batteries using the Single Particle Model with Electrolyte →
- PossiblePossibly related (embedding) · 53%Long-Range Machine Learning of Electron Density for Twisted Bilayer Moir\'e Materials - APS Journals →
- PossiblePossibly related (embedding) · 52%Artificial intelligence and quantum chemistry unveil next-generation "dual-modulated" catalysts for fuel cells - EurekAlert! →
- PossiblePossibly related (embedding) · 46%With Machine Learning, LLNL Researchers Embrace the Atomic-Scale Complexity of Batteries | Newswise - Newswise →
- PossiblePossibly related (embedding) · 52%Machine learning helps optimize energy storage at the atomic scale - Inspenet →
- PossiblePossibly related (embedding) · 52%Machine learning maps the atomic complexity inside batteries - Nanowerk →
Implements
Covers
Covers (incoming)
newsLong-Range Machine Learning of Electron Density for Twisted Bilayer Moir\'e Materials - APS JournalsnewsArtificial intelligence and quantum chemistry unveil next-generation "dual-modulated" catalysts for fuel cells - EurekAlert!newsWith Machine Learning, LLNL Researchers Embrace the Atomic-Scale Complexity of Batteries | Newswise - NewswisenewsMachine learning helps optimize energy storage at the atomic scale - InspenetnewsMachine learning maps the atomic complexity inside batteries - NanowerknewsVibrational power spectra as a tool to benchmark universal machine-learning interatomic potentials for molecular systems: the OMOL-1k-MD data set - Nature
Implements (incoming)
Related across the graph
newsMachine learning helps optimize energy storage at the atomic scale - InspenetpaperBeyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) BenchmarkpaperPhysics-Informed Neural Network with Transfer Learning for State Estimation in Lithium-Ion Batteries using the Single Particle Model with ElectrolytenewsWith Machine Learning, LLNL Researchers Embrace the Atomic-Scale Complexity of Batteries | Newswise - NewswisenewsGuiding generative models to uncover diverse and novel crystals via reinforcement learningnewsVibrational power spectra as a tool to benchmark universal machine-learning interatomic potentials for molecular systems: the OMOL-1k-MD data set - NaturepaperActive rejection enables reliable generalization of universal machine-learning interatomic potentialsnewsMachine learning maps the atomic complexity inside batteries - NanowerknewsArtificial intelligence and quantum chemistry unveil next-generation "dual-modulated" catalysts for fuel cells - EurekAlert!paperCatRetriever: Contrastive Representation Learning for Slab-to-Bulk Retrieval in Generative Catalyst DiscoverynewsLong-Range Machine Learning of Electron Density for Twisted Bilayer Moir\'e Materials - APS Journals