ML-COHP: predicting bond descriptors using latent space of a universal interatomic potentials

Abstract

The integrated crystal orbital Hamilton population (ICOHP) is the standard measure of bond strength in a DFT calculation, but every structure needs its own DFT single point and LOBSTER projection, which takes days for a cell of a hundred atoms, such as a grain boundary or a catalytic nanoparticle. We predict ICOHP from the frozen descriptors of a MACE foundation model. A symmetric pair head reads the scalar and bond-projected vector channels of the two atoms and corrects a pair baseline built from the element embeddings of the same model. We tested the model on the high-entropy diboride (Hf,Zr,Ti,Nb,Ta)B$_2$, Pt clusters on defective graphene and Pt/Pd nanoparticles with adsorbed O$_2$. The error on bonds stronger than 0.5 eV is 1.6-4.1%, about half that of the Gaussian-process model of Ferreira et al. trained on the same data. Trained only on high-temperature HEB$_2$ snapshots, the model reproduces the LOBSTER ICOHP of relaxed grain boundary cells to 0.06 eV in about a second, against 5-51 hours of LOBSTER. The vector channels halve the error compared to the scalar ones, and the baseline keeps the model usable with two training structures. With geometries from a fine-tuned MACE potential, the model gives the bonding of Pt/Pd particles over a continuous O$_2$ coverage, and DFT on these particles shows that O$_2$ costs a Pt-rich shell several times more metal--metal bonding than a Pd shell.