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.

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Cited by in corpus (5)

• MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows
• Energy-conserving molecular dynamics is not energy conserving
• Machine learning meets $\mathfrak{su}(n)$ Lie algebra: Enhancing quantum dynamics learning with exact trace conservation
• Toward Quantum-Aware Machine Learning: Improved Prediction of Quantum Dissipative Dynamics via Complex Valued Neural Networks
• Artificial Intelligence for Direct Prediction of Molecular Dynamics Across Chemical Space