awesome-ai4chem
github.com/sherrylixuecheng/awesome-ai4chem ↗Awesome AI for chemistry papers
49
GitHub Stars
82
Curated Resources
7
Categories
1 hour ago
Last Refreshed
Reviews ^Books ^Retrosynthesis ^Molecular Dynamics ^Property learning ^Generalized Model/Datasets ^Automated Experiments ^
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me force field design resources from awesome-ai4chem"
Installation instructions →What's inside
Automated Experiments ^
Molecular Dynamics ^
- ANI-1: An extensible neural network potential with DFT accuracy at force field computational costForce Field Design
- DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics.Force Field Design
- Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons.Force Field Design
- Generalized neural-network representation of high-dimensional potential-energy surfaces.Force Field Design
- SchNet - A deep learning architecture for molecules and materialsForce Field Design
A deep learning architecture for molecules and materials
- Teaching a neural network to attach and detach electrons from molecules.Force Field Design
Reviews ^
- Artificial intelligence applied to battery research: Hype or reality?
- Autonomous Discovery in the Chemical Sciences Part I: Progress.
- Combining machine learning and computational chemistry for predictive insights into chemical systems
- Data-driven strategies for accelerated materials design
- Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering.
- Four generations of high-dimensional neural network potentials
Books ^
- Atkins' physical chemistryChemistry
- Biological physics: Energy, information, lifeChemistry
- Chemistry: The central science 14th editionChemistry
- Deep LearningMachine Learning
- Density-functional theory of atoms and moleculesChemistry
- Essentials of Computational Chemistry: Theories and ModelsChemistry
Generalized Model/Datasets ^
- DESMILES Models & Training datasets.
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning.
- Harvard organic photovoltaic dataset(HOPV15).
- Kaggle page
- Large yet bounded: Spin gap ranges in carbenes.
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
Retrosynthesis ^
- Learning retrosynthetic planning through simulated experience.Molecular/Material Design
- Planning chemical syntheses with deep neural networks and symbolic AI.Molecular/Material Design
- Prediction of organic reaction outcomes using machine learningReaction Design
Showing a sample of 82 resources. View the full list on GitHub →