Welcome to MLatom documentation!
MLatom is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.
A video overview of the MLatom capabilities:
To get quickly started, please check a simple example illustrating the use of MLatom.
See a detailed overview of capabilities of MLatom for more information.
Tutorials
- Get started
- Density Functional Theory (DFT)
- Universal ML models
- UAIQM
- AIQM1
- DFT ensembles
- Machine learning potentials
- User-defined models
- Transfer learning
- Delta-learning
- Learning molecular dynamics
- Single-point calculations
- Geometry optimization
- Transition states
- Frequencies and thermochemistry
- Infrared spectra
- Raman spectra
- Molecular dynamics
- Quasi-classical molecular dynamics
- Vibrational spectra from MD
- Surface-hopping dynamics
- Active learning
- Data
- Periodic boundary conditions
- More tutorials
Manual for input file/CLI
- Overview
- Simulations
- Single-point calculations
- Geometry optimization
- Frequencies and thermochemistry
- IRC
- Molecular dynamics
- IR and power spectra from MD
- Simulations with universal ML-based models
- Simulations with QM methods
- Simulations with user-trained models
- Quantum dynamics with machine learning
- UV/vis spectra
- Two-photon absorption cross sections
- Learning
- Data
Manual for API