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Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments

Oliver T. Unke
Martin Stöhr
Stefan Ganscha
Thomas Unterthiner
Hartmut Maennel
Sergii Kashubin
Daniel Ahlin
Michael Gastegger
Leonardo Medrano Sandonas
Joshua T. Berryman
Alexandre Tkatchenko
Klaus-Robert Müller

April 05, 2024

Molecular dynamics (MD) simulations allow insights into complex processes, but accurate MD simulations require costly quantum-mechanical calculations. For larger systems, efficient but less reliable empirical force fields are used. Machine-learned force fields (MLFFs) offer similar accuracy as ab initio methods at orders-of-magnitude speedup, but struggle to model long-range interactions in large molecules. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations (GEMS) by training on “bottom-up” and “top-down” molecular fragments, from which the relevant interactions can be learned. GEMS allows nanosecond-scale MD simulations of >25,000 atoms at essentially ab initio quality, correctly predicts dynamical oscillations between different helical motifs in polyalanine, and yields good agreement with terahertz vibrational spectroscopy for large-scale protein-water fluctuations in solvated crambin. Our analyses indicate that simulations at ab initio accuracy might be necessary to understand dynamic biomolecular processes.