@article{TEXTUAL,
      recid = {11116},
      author = {Rico, Pablo F. Zubieta and Schneider, Ludwig and  Pérez-Lemus, Gustavo R. and Alessandri, Riccardo and  Dasetty, Siva and Nguyen, Trung D. and Menéndez, Cintia A.  and Wu, Yiheng and Jin, Yezhi and Xu, Yinan and Varner,  Samuel and Parker, John A. and Ferguson, Andrew L. and  Whitmer, Jonathan K. and de Pablo, Juan J.},
      title = {PySAGES: Flexible, advanced sampling methods accelerated  with GPUs},
      journal = {npj Computational Materials},
      address = {2024-02-14},
      number = {TEXTUAL},
      abstract = {Molecular simulations are an important tool for research  in physics, chemistry, and biology. The capabilities of  simulations can be greatly expanded by providing access to  advanced sampling methods and techniques that permit  calculation of the relevant underlying free energy  landscapes. In this sense, software that can be seamlessly  adapted to a broad range of complex systems is essential.  Building on past efforts to provide open-source  community-supported software for advanced sampling, we  introduce PySAGES, a Python implementation of the Software  Suite for Advanced General Ensemble Simulations (SSAGES)  that provides full GPU support for massively parallel  applications of enhanced sampling methods such as adaptive  biasing forces, harmonic bias, or forward flux sampling in  the context of molecular dynamics simulations. By providing  an intuitive interface that facilitates the management of a  system’s configuration, the inclusion of new collective  variables, and the implementation of sophisticated free  energy-based sampling methods, the PySAGES library serves  as a general platform for the development and  implementation of emerging simulation techniques. The  capabilities, core features, and computational performance  of this tool are demonstrated with clear and concise  examples pertaining to different classes of molecular  systems. We anticipate that PySAGES will provide the  scientific community with a robust and easily accessible  platform to accelerate simulations, improve sampling, and  enable facile estimation of free energies for a wide range  of materials and processes.},
      url = {http://knowledge.uchicago.edu/record/11116},
}