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physics.chem-ph2025
Performance of universal machine-learned potentials with explicit long-range interactions in biomolecular simulations
Viktor Zaverkin, Matheus Ferraz, Francesco Alesiani +1
Universal machine-learned potentials promise transferable accuracy across compositional and vibrational degrees of freedom, yet their application to biomolecular simulations remain…
physics.chem-ph2024★ 1 cited
Physics-Informed Weakly Supervised Learning for Interatomic Potentials
Makoto Takamoto, Viktor Zaverkin, Mathias Niepert
Machine learning plays an increasingly important role in computational chemistry and materials science, complementing computationally intensive ab initio and first-principles metho…