Metadata-Version: 2.4
Name: aitomic-addons
Version: 1.0.0
Summary: Aitomic Add-Ons for MLatom by Aitomistic
Author: Aitomistic
License: AITOMIC ADD-ONS FOR MLATOM — ACADEMIC LICENSE AGREEMENT
        
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Project-URL: Homepage, https://aitomistic.com/mlatom/addons.html
Keywords: mlatom,aiqm3,computational chemistry,machine learning
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: Other/Proprietary License
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Chemistry
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: mlatom
Requires-Dist: torch
Requires-Dist: torchani
Requires-Dist: requests
Requires-Dist: tqdm
Dynamic: license-file

# aitomic-addons

Aitomic Add-Ons for [MLatom](https://github.com/dralgroup/mlatom) by [Aitomistic](https://aitomistic.com), 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

```bash
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`](https://github.com/dftd3/simple-dftd3) separately if it is not already
available, and point the `dftd3bin` environment variable at the executable:

```bash
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:

```python
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:

```bash
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](https://aitomistic.xyz)**, where the
**[Protomia](https://aitomistic.com/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 license** — <contact@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](https://doi.org/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](https://doi.org/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>.
