Molecular simulation plays an important role in scientific research and engineering, but traditional methods have limitations in time and space scales, and it is difficult to cope with systems with complex free energy. With the development of hardware accelerators such as GPUs, researchers can perform larger and longer simulations, supported by a variety of software options. Augmented Sampling MD simulation uses machine learning techniques to improve accuracy, but a solution that fully integrates Augmented Sampling, hardware acceleration, and machine learning frameworks with GPUs is still to be developed.
fig. 1 the pysages simulation flowchart.
by Juan J., Pritzker School of Molecular Engineering, University of Chicago, USAThe team led by Prof. De Pablo launched PySages, a library for enhanced sampling in molecular dynamics simulations. It allows users to take advantage of multiple enhanced sampling methods and collective variables, and new methods can be added through a clean Python and Jax-based interface.
fig. 2 example of how to use the python interface for pysages.
The research team demonstrated how pysages can be applied through a series of examples in different fields such as drug design, materials engineering, polymer physics, and de novo molecular dynamics simulation. The authors demonstrate the library's flexibility and high-performance potential when solving diverse problems.
fig. 3 example of how to write a cv in pysages.
The analysis shows that when dealing with large problems, Pysages executes biased simulations by more than an order of magnitude faster than libraries like SSAGES, even when the backend is already using GPUs for computation.
fig. 4 dynamical undocking (duck) method in detail.
In the near future, the authors plan to optimize the calculation function on the pysages side so that it is completely asynchronous with the force calculation on the backend, which will further improve its current performance. The authors also invite the community to contribute to the development of pysages, including suggesting new features, reporting bugs, or contributing**.
fig. 5 free energy landscape of the fission of a spherical diblockcopolymer domain.
In conclusion, the authors believe that PySages provides a useful tool for researchers interested in molecular and ab initio simulations, thanks to its friendly sampling method and framework for defining and using collective variables, as well as its high performance on GPU devices.
fig. 6 free energy surface (fes) of 5cb in a hybrid anchoring slab with sds and water.
The authors are excited about the potential of pysages to enable fully end-to-end differentiable free energy computations. This will open up new possibilities for force field and material design, leading to significant advances in these areas. This article was recently published in NPJ Computational Materials
fig. 7 free energy (t = 300 k) of the na–cl distance when in solution
editorial summary
a high-performance enhanced sampling library: molecular dynamics simulations
molecular simulations play a crucial role in scientific research and engineering but traditional methods are limited by time and spatial scales, *it difficult to handle systems with complex free energy landscapes. with the advent of hardware accelerators like gpus, researchers can now conduct simulations on a larger scale and over longer periods, with many software packages **ailable to support this. enhanced sampling md simulations leverage machine learning to improve accuracy, yet a fully integrated solution combining enhanced sampling, hardware acceleration, and machine learning frameworks on gpus remains to be developed.
fig. 8 free energy calculation for different systems modeled with machine-learned force fields.
a team led by prof. juan j. de pablo from pritzker school of molecular engineering, the university of chicago, usa, introduced pysages, a library for enhanced sampling in molecular dynamics simulations, which allows users to utilize a variety of enhanced sampling methods and collective variables, as well as to implement new ones via a **python and jax-based interface. the authors showed how pysages can be used through a number of example applications in different fields such as drug design, materials engineering, polymer physics, and ab-initio md simulations. the authors hope that these convey to the reader the flexibility and potential of the library for addressing a diverse set of problems in a high-performance manner. as the analysis showcased, for large problems, pysages can perform biased simulation well over one order of magnitude faster than a library such as ssages even when the backend already performs computations on a gpu. nevertheless, as with any newly developed software, pysages will continue to undergo improvements. in the near term, the authors plan to optimize pysages-side computations to run fully asynchronously with the computation of the forces of the backend, which will further enhance its current performance. the authors also invite the community to contribute to the development of pysages, whether by suggesting new features, reporting bugs, or contributing code.
fig. 9 profiled timelines for a single-time step of unbiased and biased execution with hoomd-blue and openmm.
overall, the authors believe that pysages provides a useful tool for researchers interested in performing molecular and ab-initio simulations in multiple fields, due to its user-friendly framework for defining and using sampling methods and collective variables, as well as its high performance on gpu devices. looking further ahead, the authors are excited about the potential for pysages to enable fully end-to-end differentiable free energy calculations. this will provide new possibilities for force-field and materials design, which would drive significant advances in these areas. this article was recently published in npj computational materials 10: 35 (2024).
Original text abstract and its translation
pysages: flexible, advanced sampling methods accelerated with GPUS
pablo f. zubieta rico, ludwig schneider, gust**o r. pérez-lemus, riccardo alessandri,siva dasettytrung d. nguyencintia a. menéndezyiheng wuyezhi jinyinan xusamuel varnerjohn a. parkerandrew l. fergusonjonathan k. whitmerjuan j. de pablo
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.
Summary:
Molecular simulation is an important scientific tool for exploring physics, chemistry and biology. The capabilities of the simulation are significantly enhanced by the incorporation of advanced sampling methods and techniques that allow us to accurately calculate the associated free energy. As a result, software that can flexibly adapt to a wide range of complex systems becomes essential. Building on past efforts to provide open-source community-supported advanced sampling software, we have introduced PySages, a Python-based advanced general-purpose ensemble simulation software suite (SSAGES) that provides comprehensive support for GPU-based massively parallel applications that enable the efficient implementation of massively parallel enhanced sampling methods such as adaptive bias force, simple harmonic bias, and forward flux sampling in molecular dynamics simulations. Pysages provides a user-friendly interface that simplifies the management of system configurations, the addition of collective variables, and the implementation of advanced free energy-based sampling methods, making it a common platform for the development and application of new simulation techniques. The capabilities, key features, and computational performance of the Pysages library have been demonstrated with clear and concise examples for different types of molecular systems. We believe that pysages will provide the scientific community with a solid and easy-to-use platform to accelerate simulations, improve sampling efficiency, and easily estimate the free energy of various materials and processes."