Author

M. Domina

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Preprint Oct 2026

Unconstrained learning of equivariant atomistic properties

Machine-learned interatomic potentials based on graph neural networks (GNNs) have made first-principles accuracy routine for the potential energy surface (PES). Their backbones, often O(3)-equivariant by design, are increasingly used to learn other atomistic properties. Many of these are of high angular order or pseudo...

Joseph W. Abbott, Paolo Pegolo, Pol Febrer et al. · 0 citations

Preprint Oct 2026

Overcoming the limitations of body-ordered potentials for atomistic machine learning

Machine-learning interatomic potentials have become indispensable tools in atomistic simulations. The most popular models rely heavily on physical priors, such as locality, smoothness and symmetry, that are assumed to improve the accuracy and transferability of the trained models. However, recent evidence suggests that...

Filippo Bigi, M. Domina, Maximilian L. Ach et al. · 0 citations

#machine learning Jul 2026

Using large language models to probe the limits of atom-centered structural descriptors

This experiment exposes an extremely fruitful usage pattern for AI in science: translating results between different communities and application domains, accelerating the process by which serendipitous discoveries in a field become paradigm-shifting breakthroughs in another.

M. Domina, Michele Ceriotti · arXiv.org · 0 citations