Update: 2024.03.17

📈 Trending Up

Projects that have a higher project-quality score compared to the last update. There might be a variety of reasons, such as increased downloads or code activity.

• cdk (🥇24 · ⭐ 460 · 📈) - The Chemistry Development Kit. LGPL-2.1 cheminformatics Java
• AI for Science Resources (🥈14 · ⭐ 360 · 📈) - List of resources for AI4Science research, including learning resources. GPL-3.0 license
• QH9: A Quantum Hamiltonian Prediction Benchmark (🥈14 · ⭐ 360 · 📈) - Artificial Intelligence Research for Science (AIRS). CC-BY-NC-SA 4.0 ML-DFT
• Artificial Intelligence for Science (AIRS) (🥉14 · ⭐ 360 · 📈) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 license rep-learn generative ML-IAP MD ML-DFT ML-WFT biomolecules
• QHNet (🥈14 · ⭐ 360 · 📈) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 rep-learn

📉 Trending Down

Projects that have a lower project-quality score compared to the last update. There might be a variety of reasons such as decreased downloads or code activity.

• TorchMD-NET (🥇22 · ⭐ 270 · 📉) - Neural network potentials. MIT MD rep-learn transformer pre-trained
• DIG: Dive into Graphs (🥈21 · ⭐ 1.7K · 📉) - A library for graph deep learning research. GPL-3.0
• mlcolvar (🥈16 · ⭐ 74 · 📉) - A unified framework for machine learning collective variables for enhanced sampling simulations. MIT enhanced-sampling

➕ Added Projects

Projects that were recently added to this best-of list.

• pymatviz (🥉17 · ⭐ 78 · ➕) - A toolkit for visualizations in materials informatics. MIT general-tool probabilistic
• FAENet (🥈11 · ⭐ 21 · ➕) - MIT
• GNoME Explorer (🥉7 · ⭐ 500 · 🐣) - Graph Networks for Materials Exploration Database. Apache-2 datasets materials-discovery
• Materials Discovery: GNoME (🥈6 · ⭐ 500 · 🐣) - Apache-2 r e p - l e a r n , d a t a s e t s
• halex (🥉2 · ⭐ 1 · 🐣) - Hamiltonian Learning for Excited States https://doi.org/10.48550/arXiv.2311.00844. Unlicensed ML-WFT excited-states
• TorchMD-NET (🥈20 · ⭐ 220 · ➕) - Neural network potentials based on graph neural networks and equivariant transformers. MIT ML-IAP rep-learn tranformer pre-trained
• LLM-Prop (🥉8 · ⭐ 4 · ➕) - A repository for the LLM-Prop implementation. None found
• MLXDM (🥉7 · ⭐ 4 · 💤) - A Neural Network Potential with Rigorous Treatment of Long-Range Dispersion https://doi.org/10.1039/D2DD00150K. MIT long-range
• paper-data-redundancy (🥉7 · ⭐ 3 · 🐣) - Codes and data for the paper On the redundancy in large material datasets: efficient and robust learning with less data. BSD-3 small-data single-paper
• paper-ml-robustness-material-property (🥉4 · ⭐ 3 · 💤) - BSD-3 d a t a s e t s , s i n g l e - p a p e r
• Materials Data Facility (MDF) (🥈9 · ⭐ 10 · 💤) - A simple way to publish, discover, and access materials datasets. Publication of very large datasets supported (e.g.,.. Apache-2
• OPTIMADE Python tools (🥇25 · ⭐ 54 · ➕) - Tools for implementing and consuming OPTIMADE APIs in Python. MIT
• OPTIMADE Tutorial Exercises (🥈8 · ⭐ 11 · ➕) - Tutorial exercises for the OPTIMADE API. MIT datasets
• optimade.science (🥉8 · ⭐ 8 · ➕) - A sky-scanner Optimade browser-only GUI. MIT datasets
• Does this material exist? (🥉4 · ⭐ 2 · ➕) - Vote on whether you think predicted crystal structures could be synthesised. MIT for-fun materials-discovery
• OPTIMADE providers dashboard (🥉4 · ⭐ 1 · ➕) - A dashboard of known providers. Unlicensed
• GPUMD (🥇20 · ⭐ 300 · ➕) - GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials.. GPL-3.0 MD C++ electrostatics
• nep-data (🥉1 · ⭐ 9 · 💀) - Data related to the NEP machine-learned potential of GPUMD. Unlicensed ML-IAP MD transport-phenomena