AIRI-Institute/nablaDFT
nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset
This project provides a comprehensive collection of data and tools for computational chemists working with drug-like molecules. It includes a vast dataset of molecular structures, their energies, forces, and other quantum chemical properties, along with relaxation trajectories. Researchers can use this to train and evaluate neural network potentials for predicting molecular behavior, enabling faster and more scalable drug discovery and chemical science research.
227 stars.
Use this if you are a computational chemist or drug discovery scientist looking for a large, high-quality dataset to develop and benchmark machine learning models for predicting molecular properties or optimizing molecular conformations.
Not ideal if you are looking for an out-of-the-box software for direct molecular simulation without needing to train or evaluate neural network potentials.
Stars
227
Forks
26
Language
Python
License
MIT
Category
Last pushed
Dec 31, 2025
Commits (30d)
0
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