Conditioned transport-based machine learning techniques for simulating atomic systems

At least one processor comprising:

• processing circuitry to perform operations comprising:
• generating a first three-dimensional representation of a structural state of an atomic system at a given time step;
• generating a condition embedding associated with the atomic system, wherein the condition embedding represents a structural state of the atomic system at the given time step;
• and
• processing, using a machine learning model, the first three-dimensional representation and the condition embedding to generate a second three-dimensional representation of a structural state of the atomic system at a next time step,
• wherein the machine learning model is trained to predict a transport of one or more atomic systems between two arbitrary probability distributions and, during the processing, generates transport predictions for the atomic system at sub-time steps in between the given time step and the next time step based on a noise-perturbed latent space that is conditioned on at least the structural state of the atomic system at the given time step.

A system comprising:

• one or more processors to perform operations comprising:
• obtaining a first three-dimensional representation of a structural state of an atomic system at a given time step;
• generating, using a machine learning model and at least on the first three-dimensional representation, transport predictions at one or more sub-time steps in between the given time step and a subsequent time step based on noise-perturbed latent space conditioned on at least the structural state of the atomic system at the given time step;
• and
• generating a visual output representing a structural state of the atomic system at the subsequent time step based on the transport predictions.