PySAGES: flexible, advanced sampling methods accelerated with GPUs

• Published in: npj computational materials 2024-02, Vol.10 (1), p.35-12, Article 35
• Main Authors: Zubieta Rico, Pablo F., Schneider, Ludwig, Pérez-Lemus, Gustavo R., Alessandri, Riccardo, Dasetty, Siva, Nguyen, Trung D., Menéndez, Cintia A., Wu, Yiheng, Jin, Yezhi, Xu, Yinan, Varner, Samuel, Parker, John A., Ferguson, Andrew L., Whitmer, Jonathan K., de Pablo, Juan J.
• Format: Article
• Language: English
• Subjects: 631/57; 631/61/54/1754; 639/301/1034/1035; 639/301/1034/1036; 639/301/1034/1037; Adaptive sampling; Characterization and Evaluation of Materials; Chemistry and Materials Science; Complex systems; Computational Intelligence; Free energy; Materials Science; Mathematical and Computational Engineering; Mathematical and Computational Physics; Mathematical Modeling and Industrial Mathematics; Molecular dynamics; Sampling; Sampling methods; Simulation; Software; Software packages; Theoretical
• ISSN: 2057-3960; 2057-3960
• DOI: 10.1038/s41524-023-01189-z

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.