Machine learning force fields shows extreme generalisation

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• Professor Gabor Csanyi
• Tuesday 21 October 2025, 18:00-19:30
• Wolfson Lecture Theatre, Department of Chemistry, Lensfield Road.

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I will introduce the general problem of first principles force fields: creating surrogate models for quantum mechanics that yield the energy of a configuration of atoms in 3D space, as we would find them in materials or molecules. Over the last decade significant advances were made in the attainable accuracy, and today we can model materials and molecules with a per-atom energy accuracy of up to 1 part in 10,000 with a speedup of over a million or more compared to the explicit quantum mechanical calculation, enabling accurate molecular dynamics simulations on large length and time scales. The most surprising aspect of the best models is its extreme generalisation: fitted only on small periodic crystals, it shows stable trajectories on arbitrary chemical systems, from water to nanoparticles and proteins. The precise relationship between the architectural elements and the extreme generalisation is still a mystery. The locality of the graph neural network structure is key to its success, as well as high body order and message passing. The force fields get significantly better with more data, yet model size and complexity can remain largely the same. Current challenges include integrating explicit long range electrostatics and combining large datasets for materials and organic molecules where the appropriate levels of electronic structure theory are incompatible.

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