From sparse quantum-computing data to atomistic simulation with universal machine-learning interatomic potentials
Researchers propose a framework that refines an existing universal machine-learning interatomic potential (uMLIP) originally trained on density functional theory (DFT) data. Instead of building a quantum-computed potential from scratch, the approach uses a small number of accurate electronic-structure reference energies obtained from a quantum computer to update the pretrained potential. The work is described in a preprint posted to arXiv.
Within 0-2 years, the framework is demonstrated on small molecules or bulk materials with a few dozen quantum-computed reference energies, showing measurable improvement over the base DFT uMLIP for a specific property.