Updates
- 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
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.
Contributions and derivatives
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.