articleJournal of the American Chemical SocietyAug 31, 2025HYBRID OA

Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields

AKAdil KabyldaJTJ. Thorben FrankSSSergio Suarez DouAKAlmaz KhabibrakhmanovLMLeonardo Medrano Sandonas

University of Luxembourg · Beijing Institute of Big Data Research · +8 more institutions

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Abstract

Machine Learning Force Fields (MLFFs) promise to enable general molecular simulations that can simultaneously achieve efficiency, accuracy, transferability, and scalability for diverse molecules, materials, and hybrid interfaces.A key step toward this goal has been made with the GEMS approach to biomolecular dynamics [Unke et al., Sci.Adv.2024, 10, eadn4397].This work introduces the SO3LR method that integrates the fast and stable SO3krates neural network for semilocal interactions with universal pairwise force fields designed for short-range repulsion, long-range electrostatics, and dispersion interactions.SO3LR is trained on a diverse set of 4 million neutral and charged molecular complexes computed at the…

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44
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100%
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9

Topics & keywords

Topics
Keywords
  • Chemistry
  • Pairwise comparison
  • Artificial neural network
  • Force field (fiction)
  • Molecular dynamics
  • Statistical physics
  • Artificial intelligence
  • Computational chemistry
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