• MLatom 3.18.0 - FSSH, KRR in Julia, MDtrajNet-1 - universal model for directly predicting MD trajectories, ECTS - a diffusion model for generating TSs.
• MLatom 3.17.1-3.17.2 (26.03-21.05.2025) - + QST2 and QST3, NEB, more options for RMSD calculations and UV/vis plots, code refactoring for higher efficiency, major bug fixes, etc.
• MLatom 3.16.2 (18.12.2024) - bug fixes, quality of life improvements (calculation of bond lengths, angles, RMSD, geometry optimization followed by frequency calculations in the same file, etc.).
• MLatom 3.16.1 (11.12.2024) - DFTB and TD-DFTB via interface to DFTB+.
• MLatom 3.16.0 (04.12.2024) - TDDFT and TDA calculations via PySCF interface, TDDFT via Gaussian, and parsing of Gaussian output files into MLatom data format (overview)
• MLatom 3.15.0 (27.11.2024) - fine-tuning of the foundational ANI potentials ANI-1x, ANI-1ccx, ANI-1ccx-gelu, and ANI-2x.
• MLatom 3.14.0 (20.11.2024) - UV/vis spectra from single-point convolution and nuclear-ensemble approach. Updated interface to MACE to support its latest 0.3.8 version.
• MLatom 3.13.0 (06.11.2024) - IR spectra calculations with AIQM1, AIQM2, UAIQM with semi-empirical baseline, and a range of QM methods (DFT, semi-empirical, ab initio wavefunction), with empirical scaling for better accuracy, special spectra module with plotting routines in Python.
• MLatom 3.12.0 (08.10.2024) - AIQM2, ANI-1ccx-gelu.
• MLatom 3.11.0 (23.09.2024) - DENS24 functionals, simpler choice of methods, IR spectra, important bug fixes (particularly for active learning) (overview).
• MLatom 3.10.0-1 (21-22.08.2024) - active learning for surface hopping MD, multi-state ANI for excited states, gapMD for efficient exploration of the conical intersection regions, quality of life improvements such as viewing molecules, databases, and trajectories in Jupyter, easier load of molecules (overview).
• A-MLatom/MLatom@XACS update (24.07.2024) - Raman spectra
• MLatom 3.9.0 (23.07.2024) - periodic boundary conditions
• MLatom 3.8.0 (17.07.2024) - directly learning dynamics
• MLatom 3.7.0-1 (03-04.07.2024) - active learning & batch parallelization of MD
• A-MLatom/MLatom@XACS update (27.06.2024) - universal and updatable AI-enhanced QM methods (UAIQM)
• A-MLatom/MLatom@XACS update (20.06.2024) - IR spectra
• MLatom 3.6.0 (15.05.2024) - + new universal ML models (ANI-1xnr, AIMnet2, DM21)
• MLatom 3.5.0 (08.05.2024) - quasi-classical trajectory/molecular dynamics
• MLatom 3.4.0 (29.04.2024) - usability improvements with focus on geometry optimizations
• MLatom 3.3.0 (03.04.2024) - surface-hopping dynamics
• MLatom 3.2.0 (19.03.2024) - diffusion Monte Carlo and energy-weighted training
• MLatom 3.1.0 (12.29.2023) - MACE interface
• MLatom 3.0.0 (12.09.2023)

MLatom is a package for atomistic simulations with machine learning. See official website MLatom.com for more information, manuals and tutorials.

It is an open-source software under the MIT license (modified to request proper citations). MLatom is a part of XACS (Xiamen Atomistic Computing Suite) since 2022 and you can use MLatom @ XACS cloud computing service for using the package online via web browser.

The MLatom can be also conveniently installed via pip:

python3 -m pip install -U MLatom

Dependences may need to be also installed as described on the official website.

To install the package from source (e.g., for development), you will need a Fortran compiler (like gfortran) installed and available in your system's PATH.

The installation is then handled by pip. From the root directory of this project, run:

# For a standard installation
python3 -m pip install .

# Or for an editable (development) installation
python3 -m pip install -e .

This process will automatically compile the necessary Fortran components and install the mlatom package.

We highly welcome the contributions to the MLatom project. You may also create your own private derivatives of the project by following the license requirements.

If you want to contribute to the main MLatom repository, the easiest way is to create a fork and then send a pull request. Alternatively, you can ask us to create a branch for you. After we receive a pull request, we will review the submitted modifications to the code and may clean up of the code and do other changes to it and eventually include your modifications in the main repository and the official release.