Giovanni Pizzi

Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning

This work demonstrates how recent foundational machine learning interatomic potentials (MLIPs) trained at the r$^2$SCAN level can be leveraged to improve the agreement of formation energies with experiment, reducing the mean absolute error by more than 40% relative to GGA without requiring any additional DFT calculation.