分子模拟在科学研究和工程中发挥着重要作用，但传统方法在时间和空间尺度上存在局限性，难以应对具有复杂自由能的系统。 随着 GPU 等硬件加速器的发展，研究人员可以在各种软件选项的支持下执行更大、更长的模拟。 增强采样 MD 仿真使用机器学习技术来提高准确性，但将增强采样、硬件加速和机器学习框架与 GPU 完全集成的解决方案仍有待开发。
fig. 1 the pysages simulation flowchart.
作者：Juan J.，美国芝加哥大学普利兹克分子工程学院由De Pablo教授领导的团队推出了PySages，这是一个用于在分子动力学模拟中增强采样的库。 它允许用户利用多种增强的采样方法和集合变量，并且可以通过基于 Python 和 Jax 的干净接口添加新方法。
fig. 2 example of how to use the python interface for pysages.
研究团队通过一系列示例展示了如何在药物设计、材料工程、高分子物理和从头分子动力学模拟等不同领域应用pysages。 作者展示了该库在解决各种问题时的灵活性和高性能潜力。
fig. 3 example of how to write a cv in pysages.
分析表明，在处理大型问题时，Pysages 执行偏差模拟的速度比 SSAGES 等库快一个数量级以上，即使后端已经在使用 GPU 进行计算也是如此。
fig. 4 dynamical undocking (duck) method in detail.
在不久的将来，作者计划在pysages端优化计算函数，使其与后端的力计算完全异步，这将进一步提高其当前性能。 作者还邀请社区为 pysages 的开发做出贡献，包括建议新功能、报告错误或贡献**。
fig. 5 free energy landscape of the fission of a spherical diblockcopolymer domain.
总之，作者认为，PySages为对分子和从头开始模拟感兴趣的研究人员提供了一个有用的工具，这要归功于其友好的采样方法和用于定义和使用集体变量的框架，以及它在GPU设备上的高性能。
fig. 6 free energy surface (fes) of 5cb in a hybrid anchoring slab with sds and water.
作者对pysages实现完全端到端可微自由能计算的潜力感到兴奋。 这将为力场和材料设计开辟新的可能性，从而在这些领域取得重大进展。 本文最近发表在NPJ Computational Materials上
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).
原文摘要及其译文
pysages：使用 GPUS 加速的灵活、先进的采样方法
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.
总结：
分子模拟是探索物理、化学和生物学的重要科学工具。 通过结合先进的采样方法和技术，模拟的能力得到了显着增强，使我们能够准确计算相关的自由能。 因此，能够灵活适应各种复杂系统的软件变得至关重要。 在过去提供开源社区支持的高级采样软件的基础上，我们推出了 PySages，这是一个基于 Python 的高级通用集成仿真软件套件 （SSAGES），它为基于 GPU 的大规模并行应用程序提供全面支持，从而能够高效实现大规模并行增强采样方法，例如分子动力学模拟中的自适应偏置力、简单谐波偏置和正向通量采样。 Pysages提供了一个用户友好的界面，简化了系统配置的管理，增加了集体变量，并实现了先进的基于自由能的采样方法，使其成为开发和应用新模拟技术的通用平台。 Pysages 库的功能、关键特性和计算性能已经通过针对不同类型分子系统的清晰简洁的示例进行了演示。 我们相信，pysages将为科学界提供一个坚实且易于使用的平台，以加速模拟，提高采样效率，并轻松估计各种材料和工艺的自由能。