mlatom 3.23.4

A Package for AI-enhanced computational chemistry

MLatom

Version 3.23.4

MLatom is an open-source package for atomistic simulations with machine learning and quantum chemical methods — DFT, wavefunction-based, and semi-empirical approximations. Use it as a Python library, through input files, or from the command line — run it locally, or online with no installation.

Website: http://mlatom.com · Documentation: http://mlatom.com/docs · GitHub: https://github.com/dralgroup/mlatom

Run online — no installation

Run MLatom in your browser on either online platform — both powered by Protomia, with an AI assistant for autonomous atomistic simulations:

• Aitomistic Hub — registration-free.
• Aitomistic Lab@XMU — free for academic users (registration with an academic email).

Installation

MLatom runs on Linux and requires Python 3.9 or newer. Install and upgrade via pip:

pip install -U mlatom

That pulls everything needed to import and run MLatom, including the ML backends. The AIQM2 quick start below additionally needs the DFT-D4 program, which is distributed via conda:

conda install -c conda-forge dftd4
export dftd4bin=$(which dftd4)

See the installation guide for optional interfaces and other methods. Advanced Aitomistic methods such as AIQM3 are available through the Aitomic add-ons.

Quick start

Optimize the geometry of a water molecule with AIQM2 — an AI-enhanced quantum-mechanical method (native to MLatom, CHNO elements) that reaches beyond-DFT accuracy at semi-empirical cost:

import mlatom as ml

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
''')
aiqm2 = ml.methods(method='AIQM2')
opt = ml.optimize_geometry(model=aiqm2, initial_molecule=mol).optimized_molecule
print(opt.energy)          # optimized energy in hartree (≈ -76.3838)

Prefer zero setup? Run it online on the Aitomistic Hub or Aitomistic Lab@XMU — no installation needed.

Features

• Methods — universal ML potentials (ANI, AIMNet2); AI-enhanced QM methods (AIQM1/AIQM2, UAIQM) approaching coupled-cluster accuracy at semi-empirical cost; and DFT, semi-empirical, and wavefunction methods via interfaces (PySCF, Gaussian, ORCA, xtb, MNDO, Turbomole, DFTB+, Sparrow, Columbus).
• Simulations — geometry optimization, transition-state search, IRC, frequencies and thermochemistry, molecular dynamics, surface-hopping nonadiabatic dynamics, IR/Raman/UV–vis spectra, and periodic boundary conditions.
• ML models — train and use KREG, GAP-SOAP, ANI, MACE, and more, with active learning, Δ-learning, transfer learning, and self-correction.

Full manuals and tutorials are at mlatom.com/docs.

Using with AI agents

MLatom can be driven by AI agents. Protomia is the main assistant and runs online with nothing to install; Aitomia is an agentic system academic users can install locally; and MLatom Skills is an open, agent-agnostic collection of skills for your own AI coding assistant (Protomia, Claude, Cursor, GitHub Copilot, ...). The MLatom repository also ships an AGENTS.md to orient your agent. See Using MLatom with AI agents for an overview.

License and citations

License

MLatom is open-source software under the Apache License 2.0.

Copyright 2013-2026 Pavlo O. Dral (http://dr-dral.com/)

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this software except in compliance with the License. You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0.

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Citations

If you use MLatom in scientific work, please cite it. For convenience, the citations are provided below in BibTeX format.

@article{MLatom 3,
author = {Dral, Pavlo O. and Ge, Fuchun and Hou, Yi-Fan and Zheng, Peikun and Chen, Yuxinxin and Barbatti, Mario and Isayev, Olexandr and Wang, Cheng and Xue, Bao-Xin and Pinheiro Jr, Max and Su, Yuming and Dai, Yiheng and Chen, Yangtao and Zhang, Shuang and Zhang, Lina and Ullah, Arif and Zhang, Quanhao and Ou, Yanchi},
title = {MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows},
journal = {J. Chem. Theory Comput.},
volume = {20},
number = {3},
pages = {1193--1213},
DOI = {10.1021/acs.jctc.3c01203},
year = {2024},
type = {Journal Article}
}

@misc{MLatomProg,
author = {Dral, Pavlo O. and Ge, Fuchun and Hou, Yi-Fan and Chen, Yuxinxin and Zheng, Peikun and Xue, Bao-Xin and Martyka, Mikolaj and Zhang, Lina and Martinka, Jakub and Zhang, Quanhao and Tong, Xin-Yu and Ullah, Arif and Pios, Sebastian V. and Kumar, Vignesh B. and Ou, Yanchi and Jr, Max Pinheiro and Su, Yuming and Dai, Yiheng and Chen, Yangtao and Zhang, Shuang and Hu, Jinming and Bispo, Matheus O.},
title = {MLatom: A Package for Atomistic Simulations with Machine Learning},
note = {version 3.23.4, Xiamen University, Xiamen, China},
year = {2013--2026},
type = {Computer Program}
}