3 research outputs found
Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models
We demonstrate that AI can learn atomistic systems in the four-dimensional
(4D) spacetime. For this, we introduce the 4D-spacetime GICnet model which for
the given initial conditions - nuclear positions and velocities at time zero -
can predict nuclear positions and velocities as a continuous function of time
up to the distant future. Such models of molecules can be unrolled in the time
dimension to yield long-time high-resolution molecular dynamics trajectories
with high efficiency and accuracy. 4D-spacetime models can make predictions for
different times in any order and do not need a stepwise evaluation of forces
and integration of the equations of motions at discretized time steps, which is
a major advance over the traditional, cost-inefficient molecular dynamics.
These models can be used to speed up dynamics, simulate vibrational spectra,
and obtain deeper insight into nuclear motions as we demonstrate for a series
of organic molecules
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
The pre-trained GICnet models from "Four-dimensional-spacetime atomistic artificial intelligence models"
The pre-trained GICnet models from:Fuchun Ge, Lina Zhang, Arif Ullah, Pavlo O. Dral*. Four-dimensional spacetime atomistic artificial intelligence models. J. Phys. Chem. Lett. 2023. DOI: 10.1021/acs.jpclett.3c01592. See also preprint on ChemRxiv.These models can be used to propagate molecular dynamics trajectories with the 4D-spacetime GICnet models. See https://github.com/dralgroup/mlatom/tree/gicnet for the instructions.</p