PySAGES: flexible, advanced sampling methods accelerated with GPUs
arXiv:2301.04835 · doi:10.1038/s41524-023-01189-z
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 new 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.
References in corpus (18)
- Separable Dual Space Gaussian Pseudo-potentials
- Escaping free-energy minima
- CP2K: An Electronic Structure and Molecular Dynamics Software Package -- Quickstep: Efficient and Accurate Electronic Structure Calculations
- PLUMED 2: New feathers for an old bird
- Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method
- Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
- Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
- Sampling rare switching events in biochemical networks
- Enhanced Sampling in Molecular Dynamics Using Metadynamics, Replica-Exchange, and Temperature-Acceleration
- Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
- Simulating rare events in equilibrium or non-equilibrium stochastic systems
- Molecular enhanced sampling with autoencoders: On-the-fly collective variable discovery and accelerated free energy landscape exploration
- Nonlinear Discovery of Slow Molecular Modes using State-Free Reversible VAMPnets
- Studying Rare Events using Forward-Flux Sampling: Recent Breakthroughs and Future Outlook
- Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics
- Learning Free Energy Landscapes Using Artificial Neural Networks
- Learning Pair Potentials using Differentiable Simulations
- Capabilities and Limitations of Time-lagged Autoencoders for Slow Mode Discovery in Dynamical Systems