Author

Alexandre Tkatchenko

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Preprint Oct 2026

Atoms that Matter: Largest Materials Datasets Are 99% Redundant, and Local Chemistry Can Guide the Required 1%

General-purpose machine-learning force fields (MLFFs) for materials are now trained on the order of $100$ million structures, assuming transferability requires such scale. We test this premise on OMat24, the largest available materials dataset, by training MACE models on subsets of $6.5$ million atoms, less than $1\%$...

Evgeny Moerman, Igor Poltavsky, Anton Charkin-Gorbulin et al. · 0 citations

#machine learning Preprint Sep 2026

A strategic roadmap for an atomistic machine-learning ecosystem

Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespread and impactful. This success is due largely to the existence of a well-developed and established physics-based modeling framework, ranging...

Jörg Behler, Michele Ceriotti, Cecilia Clementi et al. · 0 citations

Preprint Aug 2026

Accurate and Transferable Intermolecular Potential Based on Machine-Learned Molecular Electron Density

DensIP is introduced, a physics-based model of intermolecular interactions that uses machine-learned electron densities and only four universal parameters that outperforms state-of-the-art general-purpose MLFFs for long-range interactions and can be applied to molecules as large as drug ligands.

Dahvyd Wing, Mihail Bogojeski, Szabolcs Góger et al. · 1 citation