27714

PySAGES: flexible, advanced sampling methods accelerated with GPUs

Pablo F. Zubieta Rico, Ludwig Schneider, Gustavo Perez-Lemus, Riccardo Alessandri, Siva Dasetty, Cintia A. Menéndez, Yiheng Wu, Yezhi Jin, Trung Nguyen, John Parker, Andrew L. Ferguson, Juan J. de Pablo
Pritzker School of Molecular Engineering, The University of Chicago, 5640 South Ellis Avenue, Chicago, 60637, IL, USA
arXiv:2301.04835 [physics.comp-ph], (12 Jan 2023)

@misc{https://doi.org/10.48550/arxiv.2301.04835,

   doi={10.48550/ARXIV.2301.04835},

   url={https://arxiv.org/abs/2301.04835},

   author={Rico, Pablo F. Zubieta and Schneider, Ludwig and Perez-Lemus, Gustavo and Alessandri, Riccardo and Dasetty, Siva and Menéndez, Cintia A. and Wu, Yiheng and Jin, Yezhi and Nguyen, Trung and Parker, John and Ferguson, Andrew L. and de Pablo, Juan J.},

   keywords={Computational Physics (physics.comp-ph), FOS: Physical sciences, FOS: Physical sciences},

   title={PySAGES: flexible, advanced sampling methods accelerated with GPUs},

   publisher={arXiv},

   year={2023},

   copyright={Creative Commons Attribution 4.0 International}

}

Molecular dynamics simulations are a core element of research in physics, chemistry and biology. A key aspect for extending the capability of simulation tools is providing access to advanced sampling methods and techniques that permit calculation of the relevant, underlying free energy landscapes. In this sense, software tools that can be seamlessly adapted to a broad range of complex systems are essential. Building on past efforts to provide an open-source community supported software for advanced sampling, we introduce PySAGES, a Python implementation of the SSAGES that provides full support of GPU for massively parallel applications of enhanced sampling methods such as adaptive biasing forces, harmonic bias, and forward flux sampling in the context of molecular dynamics simulations. By providing an intuitive interface that facilitates the treatment of the configuration of the system, the inclusion of new collective variables, and the implementation of sophisticated free energy methods, the PySAGES library will serve as a general platform for development and implementation of emerging simulation algorithms. The capabilities and core features of this new tool are demonstrated with clear and concise examples pertaining to different classes of molecular systems.
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