Simulating catalysis: Multiscale embedding of machine learning potentials

模拟催化：机器学习潜力的多尺度嵌入

• 批准号: EP/V011421/1
• 负责人: Marc Van Der Kamp
• 金额: $ 49.9万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV011421%2F1
• 关键词: Simulating; catalysis; Multiscale; embedding; machine

In the recent decades, computer simulations have become an essential part of the molecular scientist's toolbox. However, as for any computational method, molecular simulations require a compromise between speed and precision. The most precise techniques apply principles of quantum mechanics (QM) to the molecular systems and can precisely describe processes involving changes in the electronic structure, such as the breaking and forming of chemical bonds. However, they require tremendous computer resources, being prohibitively costly even for systems containing only several hundreds of atoms. On the other extreme are highly simplified "Molecular Mechanics" (MM) methods that ignore the quantum nature of molecules and instead describe the atoms as charged "balls" of certain size connected with springs representing the chemical bonds. The core limitation of MM is its inability to describe breaking/forming of chemical bonds, therefore making it unsuitable for simulating chemical reactions. This drawback motivated the invention of combined "multiscale" models that rely on precise but expensive QM calculations to describe the part of the simulation system where the chemical reaction takes place, while treating the rest of the system with an efficient MM method. This "Quantum Mechanics/Molecular Mechanics" approach (QM/MM), honoured by the Nobel Prize in Chemistry in 2013, is now the state-of-the-art simulation technique for reactions in complex environments, such as those happening inside living organisms. Such simulations are important to understand and design catalysts, which increase the rate of chemical reactions (and can thereby reduce the amount of energy and resources required to produce molecules). However, QM/MM calculations are still only as fast as the QM method used, limiting dramatically the precision and timescale of the simulations.A completely different approach is to employ techniques from the rapidly evolving field of machine learning (ML) and construct a method that can learn and then predict the outcome of a QM calculation. Once properly trained, an ML model can provide results with QM quality, but several orders of magnitude faster. However, ML models are still significantly slower than MM ones. Therefore, a multiscale "ML/MM" model would still offer huge savings of computer time compared to pure ML simulations. Unfortunately, however, existing ML training schemes are only suitable for calculations in gas phase and cannot take into account the presence of an MM environment. The goal of the proposed research project is to develop a novel multiscale embedding approach that will allow the use of ML models as part of a ML/MM scheme. This will enable molecular simulations of unprecedented precision on processes with high complexity without limiting the detailed exploration of molecular conformations. To achieve this goal, we will take advantage of recent advances in machine learning and understanding of intermolecular interactions to develop a specialised ML workflow that predicts the interaction energy between the molecule described by ML and the MM environment. The workflow will be implemented as an open, publicly available software package that allows to train ML/MM models and run ML/MM molecular dynamics simulations of complex chemical processes, such as catalysed reactions. We expect this package to be readily adopted by a wide community of computational chemists working on enzymatic reactions, homo/heterogeneous catalysis and generally on processes in condensed phases, aided by specific training materials and workshops that we will provide. This will allow, for example, the development efficient computational workflows to understand and help design catalysts for more environmentally friendly production of desired molecules.

近几十年来，计算机模拟已经成为分子科学家工具箱中必不可少的一部分。然而，对于任何计算方法，分子模拟都需要在速度和精度之间做出妥协。最精确的技术将量子力学(QM)的原理应用于分子系统，可以精确地描述涉及电子结构变化的过程，如化学键的断裂和形成。然而，它们需要巨大的计算机资源，即使对于只包含数百个原子的系统来说，成本也高得令人望而却步。另一种极端的方法是高度简化的“分子力学”(MM)方法，这种方法忽略了分子的量子性质，而是将原子描述为带有一定大小的带电“球”，与代表化学键的弹簧相连。MM的核心缺陷是不能描述化学键的断裂/形成，因此不适合于模拟化学反应。这一缺陷促使了组合“多尺度”模型的发明，这些模型依赖于精确但昂贵的QM计算来描述模拟系统中发生化学反应的部分，同时用有效的MM方法处理系统的其余部分。这种“量子力学/分子力学”方法(QM/MM)于2013年获得诺贝尔化学奖，现在是复杂环境中反应的最先进的模拟技术，例如发生在生物体内的反应。这样的模拟对于理解和设计催化剂很重要，催化剂可以提高化学反应的速度(从而可以减少生产分子所需的能量和资源)。然而，QM/MM计算的速度仍然与QM方法一样快，这极大地限制了模拟的精度和时间尺度。另一种完全不同的方法是利用快速发展的机器学习(ML)领域的技术，构建一种可以学习并预测QM计算结果的方法。一旦训练得当，ML模型可以提供具有QM质量的结果，但速度要快几个数量级。然而，ML模型仍然比MM模型慢得多。因此，与纯ML模拟相比，多尺度的“ML/MM”模型仍然可以节省大量的计算机时间。然而，遗憾的是，现有的最大似然训练方案只适用于气相计算，不能考虑MM环境的存在。提出的研究项目的目标是开发一种新的多尺度嵌入方法，该方法将允许使用ML模型作为ML/MM方案的一部分。这将使分子模拟在高复杂性的过程中具有前所未有的精度，而不会限制对分子构象的详细探索。为了实现这一目标，我们将利用机器学习和了解分子间相互作用的最新进展，开发一种专门的ML工作流，预测ML所描述的分子与MM环境之间的相互作用能量。该工作流程将作为一个开放的、公开可用的软件包实施，该软件包允许训练ML/MM模型并运行复杂化学过程的ML/MM分子动力学模拟，例如催化反应。我们预计，在我们将提供的特定培训材料和研讨会的帮助下，这个软件包将容易被从事酶反应、同/多相催化以及一般凝聚相过程的计算化学家的广泛社区采用。例如，这将允许开发高效的计算工作流程来了解并帮助设计催化剂，以更环保地生产所需的分子。

项目成果

emle-engine: a flexible electrostatic machine learning embedding package for multiscale molecular dynamics simulations

emle-engine：用于多尺度分子动力学模拟的灵活静电机器学习嵌入包

• DOI: 10.26434/chemrxiv-2023-6rng3
• 发表时间: 2023
• 作者: Zinovjev K

Free energy along drug-protein binding pathways interactively sampled in virtual reality.

• DOI: 10.1038/s41598-023-43523-x
• 发表时间: 2023-10-04
• 期刊: SCIENTIFIC REPORTS
• 影响因子: 4.6
• 作者: Deeks, Helen M.;Zinovjev, Kirill;Barnoud, Jonathan;Mulholland, Adrian J.;van der Kamp, Marc W.;Glowacki, David R.
• 通讯作者: Glowacki, David R.

Electrostatic Embedding of Machine Learning Potentials.

• DOI: 10.1021/acs.jctc.2c00914
• 发表时间: 2023-03-28
• 期刊: JOURNAL OF CHEMICAL THEORY AND COMPUTATION
• 影响因子: 5.5
• 作者: Zinovjev, Kirill