分子模拟在科研和工程中占据重要地位，但传统方法在时间和空间尺度上存在局限，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，研究人员可进行更大规模和更长时间的模拟，且已有多款软件支持。增强采样MD模拟借助机器学习技术提高精度，但完整集成增强采样、硬件加速与机器学习框架于GPU的解决方案仍待开发。
Fig. 1 The PySAGES simulation flowchart.
由美国芝加哥大学普利兹克分子工程学院的Juan J. de Pablo教授领导的团队推出了PySAGES，这是一个用于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采样方法和集体变量，并且可以通过一个简洁的基于Python和JAX的界面来实现新方法的添加。
该研究团队展示了在药物设计、材料工程、聚合物物理学和从头算分子动力学模拟等不同领域，如何通过一系列实例应用PySAGES。作者展示了该库在解决多样化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，即使在后端已经使用GPU进行计算的情况下，PySAGES执行带偏置的模拟速度也要比像SSAGES这样的库快一个数量级以上。
Fig. 4 Dynamical Undocking (DuCK) method in detail.
近期，作者计划优化PySAGES端的计算功能，使其与后端的力计算完全异步进行，这将进一步提升其当前性能。作者还邀请社区为PySAGES的开发献计献策，包括提出新功能建议、报告错误或贡献代码。
Fig. 5 Free energy landscape of the fission of a spherical diblockcopolymer domain.
总之，作者认为，得益于其友好的采样方法和集体变量定义及使用框架，以及在GPU设备上的高性能，PySAGES为有意进行分子和从头算模拟的研究者提供了一种实用工具。
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分的自由能计算的潜力感到兴奋。这将为力场和材料设计提供新的可能性，从而推动这些领域的重大进展。该文近期发表于npj Computational Materials 10: 35 (2024).
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, making 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 available 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 simple 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Abstract及其翻译
PySAGES: flexible, Advanced sampling methods accelerated with GPUs （PySAGES：利用GPU加速的灵活高级采样方法）
Pablo F. Zubieta Rico, Ludwig Schneider, Gustavo R. Pérez-Lemus, Riccardo Alessandri,Siva Dasetty, Trung D. Nguyen, Cintia A. Menéndez, Yiheng Wu, Yezhi Jin, Yinan Xu, Samuel Varner, John A. Parker, Andrew L. Ferguson, Jonathan K. Whitmer & Juan 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.
摘要
分子模拟是探索物理、化学和生物学的重要科研工具。模拟的能力通过融入先进的采样方法和技巧得以显著提升，这些方法和技术使我们能够精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。基于过去为提供开源社区支持的高级采样软件所做的努力，我们引入了PySAGES，这是一个基于Python的高级通用系综模拟软件套件（SSAGES），它为基于GPU的大规模并行应用提供了全面支持，能够在分子动力学模拟中高效实施自适应偏置力、简谐偏置和正向通量采样等大规模并行增强采样方法。PySAGES提供了一个用户友好的界面，简化了系统配置的管理、集体变量的新增以及基于自由能的高级采样方法的实施，使其成为开发和应用新模拟技术的通用平台。PySAGES库的能力、关键特性和计算性能已通过针对不同类型分子系统的明确且简洁示例得到了展示。我们相信，PySAGES将为科学界提供一个坚实而易于使用的平台，加速模拟进程，提升采样效率，并轻松估算各种材料和过程中自由能的强有力工具。
