Supporting the OpenMM Community-led Development of Next-Generation Condensed Matter Modelling Software

支持 OpenMM 社区主导的下一代凝聚态建模软件开发

• 批准号: EP/W030276/1
• 负责人: Julien Michel
• 金额: $ 59.23万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW030276%2F1
• 关键词: Supporting; OpenMM; Community; led; Development

Atomistic simulations are the main application of high-performance computing research, and increasingly underpin innovative R&D processes in the chemical and life sciences industry. OpenMM is the fastest growing atomistic simulation engine among the current ecosystem of open-source academic software. Originally targeting a biomolecular simulation audience, the OpenMM user base is growing exponentially and has permeated diverse related domains, including materials modelling, quantum chemistry, structural bioinformatics, chemoinformatics, artificial intelligence and machine learning. The success of OpenMM is down to a design that achieves an excellent tradeoff between extensibility (via a robust user interface) and performance on GPUs (via auto generated CUDA kernels) for molecular dynamics (MD) simulations. OpenMM is used standalone or via plugins to other atomistic simulation engines, providing access to GPU-accelerated MD simulation capabilities for the whole atomistic simulation ecosystem.We have surveyed the OpenMM user community to identify its most pressing needs. OpenMM is currently maintained by a single core developer who can no longer support the training and support needs of its rapidly growing user community. We will transition OpenMM to a more sustainable community-driven development model. We will develop training resources to upskill users, and engage the community to widen participation in developing and maintaining OpenMM functionality. Machine learning (ML) potentials have the potential to revolutionise the future of atomistic simulation methodologies. Our community survey has identified strong interest in ANI neural network and GAP Gaussian process regression methods. We will deliver a self-contained GPU-optimised GAP implementation in OpenMM and coordinate with project partners working on an OpenMM ANI implementation to offer the community a library of ML potentials that can be readily plugged into existing simulation engines.OpenMM must adapt to scientific (ML potentials) and technological (increased hardware heterogeneity) drivers to continue offering its user base an optimised tradeoff between speed and ease of modification over the coming decade. We will integrate in OpenMM a multiple level intermediate representation compiler (MLIR) to auto generate from user-specified Python instructions optimised low-level code targeting diverse hardware. By enabling users to specify custom atomic featurisation techniques as OpenMM operations, which can be finely interleaved with Tensorflow or Pytorch operations, we will position OpenMM as the simulation engine of choice to support deployment of next generation ML potentials onto current GPUs and emerging AI-hardware accelerators. Our community has also required support to facilitate the combined use of independently developed OpenMM software solutions with other software from the broader atomistic simulation ecosystem. This research will develop a standardised interface to integrate OpenMM community software with CCPBioSim's interoperable Python framework BioSimSpace. We will demonstrate integration of all the work packages of this research via production of GAP ML pipelines for two use cases that target grand challenges in soft-condensed matter modeling (organocatalysis - recently recognised by the 2021 Nobel Prize in Chemistry- and protein-ligand binding).Altogether this research will position the OpenMM user community at the forefront of next-generation hybrid machine learning/molecular mechanics potentials for soft-condensed matter modelling. Deeper integrations with AI and HPC communities will pave the way for atomistic simulations to harness emerging exascale opportunities. Transitioning from a single developer to a community-driven development governance model will improve sustainability of the codebase and encourage greater adoption of OpenMM in associated academic communities and industry.

原子模拟是高性能计算研究的主要应用，并日益成为化学和生命科学行业创新研发过程的基础。OpenMM是当前开源学术软件生态系统中发展最快的原子模拟引擎。OpenMM最初的目标受众是生物分子模拟，现在它的用户群呈指数级增长，并且已经渗透到各种相关领域，包括材料建模、量子化学、结构生物信息学、化学信息学、人工智能和机器学习。OpenMM的成功在于它在分子动力学（MD）模拟的可扩展性（通过健壮的用户界面）和gpu的性能（通过自动生成CUDA内核）之间实现了很好的平衡。OpenMM可以独立使用，也可以通过插件连接到其他原子模拟引擎，为整个原子模拟生态系统提供gpu加速的MD模拟功能。我们已经调查了OpenMM用户社区，以确定其最迫切的需求。OpenMM目前由单个核心开发人员维护，他们无法再支持培训和支持其快速增长的用户社区的需求。我们将把OpenMM转变为一个更可持续的社区驱动的开发模式。我们将开发培训资源来提高用户的技能，并让社区更广泛地参与开发和维护OpenMM功能。机器学习（ML）的潜力有可能彻底改变原子模拟方法的未来。我们的社区调查已经发现了对ANI神经网络和GAP高斯过程回归方法的强烈兴趣。我们将在OpenMM中提供一个独立的gpu优化GAP实现，并与致力于OpenMM ANI实现的项目合作伙伴协调，为社区提供一个ML潜力库，可以很容易地插入到现有的模拟引擎中。OpenMM必须适应科学（ML潜力）和技术（硬件异构性增加）驱动因素，以便在未来十年继续为其用户群提供速度和易于修改之间的优化权衡。我们将在OpenMM中集成一个多级中间表示编译器（MLIR），从用户指定的Python指令自动生成针对不同硬件的优化底层代码。通过允许用户指定自定义原子特性技术作为OpenMM操作，它可以与Tensorflow或Pytorch操作精细交织，我们将OpenMM定位为首选的模拟引擎，以支持将下一代机器学习潜力部署到当前的gpu和新兴的人工智能硬件加速器上。我们的社区还需要支持，以促进独立开发的OpenMM软件解决方案与来自更广泛的原子模拟生态系统的其他软件的组合使用。该研究将开发一个标准化接口，将OpenMM社区软件与CCPBioSim的可互操作Python框架BioSimSpace集成在一起。我们将通过为两个用例生产GAP ML管道来展示本研究的所有工作包的集成，这些用例针对软凝聚态物质建模（有机催化-最近被2021年诺贝尔化学奖认可-和蛋白质配体结合）中的重大挑战。总的来说，这项研究将使OpenMM用户社区处于下一代混合机器学习/分子力学软凝聚态建模潜力的前沿。与人工智能和高性能计算社区的更深层次集成将为原子模拟铺平道路，以利用新兴的百亿亿次机会。从单个开发人员到社区驱动的开发治理模型的转变将提高代码库的可持续性，并鼓励在相关的学术社区和行业中更多地采用OpenMM。

项目成果

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.

OpenMM 8：具有机器学习潜力的分子动力学模拟。

• DOI: 10.1021/acs.jpcb.3c06662
• 发表时间: 2024
• 期刊: The journal of physical chemistry. B
• 作者: Eastman P