Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations
<|reference_start|>Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations: Machine learning potentials offer a revolutionary, unifying framework for molecular simulations across scales, from quantum chemistry to coarse-grained models. Here, I explore their potential to dramatically improve accuracy and scalability in simulating complex molecular systems. I discuss key challenges that must be addressed to fully realize their transformative potential in chemical biology and related fields.<|reference_end|>
@article{de fabritiis2024machine,
    title={Machine Learning Potentials: A Roadmap Toward Next-Generation
  Biomolecular Simulations},
    author={Gianni De Fabritiis},
    journal={arXiv preprint arXiv:2408.12625},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12625},
    primaryClass={physics.chem-ph cs.LG}
}