repoGitLabTrust 82 · PrimaryPublished 10d agoLive · 6d ago
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.
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- 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) · 51%Active rejection enables reliable generalization of universal machine-learning interatomic potentials →
- PossiblePossibly related (embedding) · 52%CatRetriever: Contrastive Representation Learning for Slab-to-Bulk Retrieval in Generative Catalyst Discovery →
- 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 →
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paperBeyond 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 learningpaperActive rejection enables reliable generalization of universal machine-learning interatomic potentialsnewsArtificial 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 Discovery