Details

Autor(en) / Beteiligte

• Ge, Fuchun
• Zhang, Lina
• Hou, Yi-Fan
• Chen, Yuxinxin
• Ullah, Arif
• Dral, Pavlo O.

Titel

Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models

Ist Teil von

• The journal of physical chemistry letters, 2023-08, Vol.14 (34), p.7732-7743

Ort / Verlag

American Chemical Society

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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 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.