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Aitomic Add-Ons for MLatom by Aitomistic

Project description

aitomic-addons

Aitomic Add-Ons for MLatom by Aitomistic, starting with AIQM3 — the third generation of delta-learning-based artificial intelligence-enhanced quantum-mechanical methods.

Free for academic, non-commercial research and teaching under the Aitomic Academic License; commercial use requires a separate license. Terms: https://aitomistic.com/mlatom/addons.html.

Tutorials: https://aitomistic.com/mlatom/tutorial_aiqm2.html.

  • Methods: AIQM3, AIQM3@DFT
  • Elements: H, C, N, O, F, S, Cl

Installation

pip install aitomic-addons

MLatom is installed automatically as a dependency. The neural-network parameters are downloaded automatically the first time a method is used.

AIQM3 also uses D3 dispersion — install dftd3 separately if it is not already available, and point the dftd3bin environment variable at the executable:

export dftd3bin=/path/to/s-dftd3

Usage

Python API

Run a calculation through MLatom's standard interface — no extra imports once the package is installed:

import mlatom as ml

# Water (XYZ coordinates in Angstrom)
mol = ml.data.molecule.from_xyz_string('''3

O    0.00000    0.00000    0.11779
H    0.00000    0.75545   -0.47116
H    0.00000   -0.75545   -0.47116
''')

aiqm3 = ml.methods(method='AIQM3')
aiqm3.predict(molecule=mol, calculate_energy=True, calculate_energy_gradients=True)

print('Energy (Hartree):', mol.energy)
print('Energy gradients (Hartree/Angstrom):')
print(mol.energy_gradients)

Output:

Energy (Hartree): -76.37869477070018
Energy gradients (Hartree/Angstrom):
[[ 1.72364427e-17  1.85195818e-16  1.57502823e-03]
 [-2.71918885e-17 -1.13356199e-03 -7.87514116e-04]
 [ 9.95544574e-18  1.13356199e-03 -7.87514116e-04]]

(On the first run, a one-time license notice is also printed.)

Without this package installed, MLatom reports AIQM3 as an add-on and points here.

Input file/CLI

AIQM3 also runs from MLatom input files. For example, a geometry optimization in a self-contained input file geomopt.inp (with the geometry inline):

AIQM3
geomopt
xyzfile='3

O    0.00000    0.00000    0.11779
H    0.00000    0.75545   -0.47116
H    0.00000   -0.75545   -0.47116
'
optxyz=water_opt.xyz

Run it as you would any MLatom job:

mlatom geomopt.inp

The optimized geometry is written to water_opt.xyz:

3

O    0.00000    0.00000    0.11608
H    0.00000    0.75745   -0.47031
H    0.00000   -0.75745   -0.47031

Online

The same calculations can also be run — without installing anything — on the Aitomistic Hub, where the Protomia AI assistant sets them up and runs them for you.

License

Aitomic Add-Ons are free for academic, non-commercial research and teaching under the Aitomic Academic License. By installing and using them you agree to the license and certify that your use is academic and non-commercial; a short one-time notice is printed the first time a method is used.

Commercial use requires a separate licensecontact@aitomistic.com. Full terms: https://aitomistic.com/mlatom/addons.html.

Citation

If you use AIQM3, please cite the method, MLatom, and Aitomic add-ons to MLatom:

  • Yuxinxin Chen, Yi-Fan Hou, Roman Zubatyuk, Olexandr Isayev, Pavlo O. Dral. AIQM3: Targeting Coupled-Cluster Accuracy with Semi-Empirical Speed across Seven Main Group Elements. J. Chem. Theory Comput. 2026. DOI: 10.1021/acs.jctc.5c01794. Preprint on ChemRxiv: https://doi.org/10.26434/chemrxiv-2025-g2dbg.

  • Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou, Peikun Zheng, Yuxinxin Chen, Mario Barbatti, Olexandr Isayev, Cheng Wang, Bao-Xin Xue, Max Pinheiro Jr, Yuming Su, Yiheng Dai, Yangtao Chen, Lina Zhang, Shuang Zhang, Arif Ullah, Quanhao Zhang, Yanchi Ou. MLatom 3: A Platform for Machine Learning-enhanced Computational Chemistry Simulations and Workflows. J. Chem. Theory Comput. 2024, 20, 1193–1213. DOI: 10.1021/acs.jctc.3c01203.

  • Pavlo O. Dral, Yuxinxin Chen, Mikołaj Martyka, Jingbai Li. Aitomic add-ons to MLatom; version 1.0.0. Aitomistic, Shenzhen, China, 2025--2026. https://aitomistic.com/mlatom/addons.html (accessed 14 June 2026).

Please also cite the underlying methods (GFN2-xTB*, TorchANI, and the dispersion correction) as listed in the MLatom output and documentation.

Staying up to date

Subscribe to the newsletter for release announcements: https://aitomistic.com/en/contact.

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