AIMNet2 Neural Network Potential
High-accuracy neural network potential for organic and elemental-organic molecules. Supports neutral and charged species with accurate predictions of energies, forces, and partial charges at wB97M-D3BJ/def2-TZVPP level of theory.
| Version | 1.0.0 |
|---|---|
| Creator | Isayev Lab |
| Delivery | executable |
| Requirements | ram: 4GB; disk: 1GB |
Features
- Single-point energy calculations
- Force predictions (analytical gradients)
- Atomic partial charges (Hirshfeld-like)
- Geometry optimization (BFGS)
- Charged species support
- Spin multiplicity support
Keywords
organic-chemistrycharged-speciesionsmoleculesdrug-discoveryneural-network-potentialDFT-accuracypartial-charges
Citation
Anstine, D.M. & Isayev, O. (2023). AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs. J. Phys. Chem. A. https://doi.org/10.1021/acs.jpca.2c06685