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Abstract

An iterative machine learning interatomic potential (MLIP) training method. The training method includes training a first multiplicity of first MLIP models in a first iteration of a training loop. The training method further includes training a second multiplicity of second MLIP models in a second iteration of the training loop in parallel with the first training step. The training method also includes combining the first MLIP models and the second MLIP models to create an iteratively trained MLIP configured to predict one or more values of a material. The MLIP may be a Gaussian Process (GP) based MLIP (e.g., FLARE). The MLIP may be a graph neural network (GNN) based MLIP (e.g., NequIP or Allegro).

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