aceANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accurate and transferable potential for organic molecules. We introduce ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies) or ANI for short. ANI is a new method designed with the intent of developing transferable neural network potentials that utilize a hig…
A deep neural network trained on DFT data learned a transferable potential that predicts total energies for organic molecules with chemical accuracy versus DFT, generalizing to systems up to 54 atoms despite training on smaller molecules.
Model scope is bounded to organic molecules with four atom types H, C, N, O and training data limited to molecules with up to 8 heavy atoms, constraining generalizability beyond that chemistry.
Evidence
- Peer-reviewedNature2023-11-29
- Peer-reviewedPhysical Review Research2020-09-16
- Peer-reviewedChemical Science2017-02-08
- Peer-reviewedNew Journal of Physics2013-09-04
How should this claim be treated?
Truvace Impact Record TRV-2026-0206, v4: “ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost.” Truvace, 2026-09-26. /record/TRV-2026-0206 (accessed at citation time). sha256 961cb7ee3665fbfc…
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