Accurate machine learning force fields via experimental and simulation
  data fusion
Sebastien Röcken Julija Zavadlav
Machine Learning (ML)-based force fields are attracting ever-increasing
interest due to their capacity to span spatiotemporal scales of classical
interatomic potentials at quantum-level accuracy. They can be trained based on
high-fidelity simulations or experiments, the former being the common case.
However, both approaches are impaired by scarce and erroneous data resulting in
models that either do not agree with well-known experimental observations or
are under-constrained and only reproduce some properties. Here we leverage both
Density Functional Theory (DFT) calculations and experimentally measured
mechanical properties and lattice parameters to train an ML potential of
titanium. We demonstrate that the fused data learning strategy can concurrently
satisfy all target objectives, thus resulting in a molecular model of higher
accuracy compared to the models trained with a single data source. The
inaccuracies of DFT functionals at target experimental properties were
corrected, while the investigated off-target properties remained largely
unperturbed. Our approach is applicable to any material and can serve as a
general strategy to obtain highly accurate ML potentials.