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Machine Learning (ML)-based force fields are attracting ever-increasinginterest due to their capacity to span spatiotemporal scales of classicalinteratomic potentials at quantum-level accuracy. They can be trained based onhigh-fidelity simulations or experiments, the former being the common case.However, both approaches are impaired by scarce and erroneous data resulting inmodels that either do not agree with well-known experimental observations orare under-constrained and only reproduce some properties. Here we leverage bothDensity Functional Theory (DFT) calculations and experimentally measuredmechanical properties and lattice parameters to train an ML potential oftitanium. We demonstrate that the fused data learning strategy can concurrentlysatisfy all target objectives, thus resulting in a molecular model of higheraccuracy compared to the models trained with a single data source. Theinaccuracies of DFT functionals at target experimental properties werecorrected, while the investigated off-target properties remained largelyunperturbed. Our approach is applicable to any material and can serve as ageneral strategy to obtain highly accurate ML potentials.

Accurate machine learning force fields via experimental and simulation data fusion