1 research outputs found
MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows
Machine learning (ML) is increasingly becoming a common tool in computational
chemistry. At the same time, the rapid development of ML methods requires a
flexible software framework for designing custom workflows. MLatom 3 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 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
pre-trained 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