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https://github.com/dralgroup/mlatom

AI-enhanced computational chemistry
https://github.com/dralgroup/mlatom

artificial-intelligence computational-chemistry deep-learning kernel-method kernel-ridge-regression machine-learning machine-learning-potential neural-network quantum-chemistry quantum-chemistry-programs quantum-mechanics

Last synced: about 2 months ago
JSON representation

AI-enhanced computational chemistry

Host: GitHub
URL: https://github.com/dralgroup/mlatom
Owner: dralgroup
License: other
Created: 2023-08-16T13:47:48.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2024-08-22T04:20:35.000Z (6 months ago)
Last Synced: 2024-08-22T13:06:28.715Z (6 months ago)
Topics: artificial-intelligence, computational-chemistry, deep-learning, kernel-method, kernel-ridge-regression, machine-learning, machine-learning-potential, neural-network, quantum-chemistry, quantum-chemistry-programs, quantum-mechanics
Language: Python
Homepage: http://mlatom.com
Size: 95 MB
Stars: 37
Watchers: 2
Forks: 6
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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best-of-atomistic-machine-learning - GitHub - 16% open · ⏱️ 18.12.2024): (General Tools)

README

        # Updates

- [MLatom 3.10.0](https://xacs.xmu.edu.cn/docs/mlatom/releases.html#mlatom-3-10-0) (21.08.2024) - 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.

- MLatom 3.9.0 (23.07.2024) - [periodic boundary conditions](https://xacs.xmu.edu.cn/docs/mlatom/releases.html#mlatom-3-9-0)

- MLatom 3.8.0 (17.07.2024) - [directly learning dynamics](https://xacs.xmu.edu.cn/docs/mlatom/releases.html#mlatom-3-8-0)

- MLatom 3.7.0-3.7.1 (03-04.07.2024) - [active learning & batch parallelization of MD](https://xacs.xmu.edu.cn/docs/mlatom/releases.html#mlatom-3-7-0-3-7-1)

- MLatom 3.6.0 (15.05.2024) - [+ new universal ML models (ANI-1xnr, AIMnet2, DM21)](https://xacs.xmu.edu.cn/docs/mlatom/releases.html#mlatom-3-6-0)

- MLatom 3.5.0 (08.05.2024) - [quasi-classical trajectory/molecular dynamics](https://xacs.xmu.edu.cn/docs/mlatom/releases.html#mlatom-3-5-0)

- MLatom 3.4.0 (29.04.2024) - [usability improvements with focus on geometry optimizations](https://xacs.xmu.edu.cn/docs/mlatom/releases.html#mlatom-3-4-0)

- MLatom 3.3.0 (03.04.2024) - [surface-hopping dynamics](https://mlatom.com/docs/releases.html#mlatom-3-3-0)

- MLatom 3.2.0 (19.03.2024) - [diffusion Monte Carlo and energy-weighted training](http://mlatom.com/docs/releases.html#mlatom-3-2-0)

- MLatom 3.1.0 (12.29.2023) - [MACE interface](http://mlatom.com/releases/#Version_31)

- MLatom 3.0.0 (12.09.2023)

# MLatom

MLatom is a package for atomistic simulations with machine learning.

See official website [MLatom.com](http://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](http://XACScloud.com/) (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.