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Maximilian L. Ach

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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
Preprint Jul 2026

Learning to Converge: Warm-Starting DFTB Self-Consistent Charges with Machine Learning

A machine learning approach is presented that accelerates DFTB simulations by predicting optimal initial atomic charges and demonstrates that ML-predicted initial charges consistently and significantly improve SCC convergence across diverse chemical systems including organic molecules, biomolecules, water clusters, tra...

Maximilian L. Ach, Karsten Reuter, C. Panosetti · 0 citations

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