Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models
Abstract
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.
Cite
@article{arxiv.2308.11311,
title = {Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models},
author = {Fuchun Ge and Lina Zhang and Yi-Fan Hou and Yuxinxin Chen and Arif Ullah and Pavlo O. Dral},
journal= {arXiv preprint arXiv:2308.11311},
year = {2023}
}