[论文解读] Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water

Manyi Yang, Luigi Bonati+2 more|arXiv (Cornell University)|Nov 23, 2020

Machine Learning in Materials Science参考文献 13被引用 7

一句话总结

本研究结合元动力学与主动学习，开发了一种机器学习势，用于模拟水溶液中尿素分解过程，实现了精确的吉布斯自由能分布，并发现了不同温度下的新型亚稳态。该方法表明，两性离子中间体的形成在酸性或碱性路径下具有相同的概率，解释了反应速率对pH不敏感的原因。

ABSTRACT

The study of chemical reactions in aqueous media is very important for its implications in several fields of science, from biology to industrial processes. Modelling these reactions is however difficult when water directly participates in the reaction. Since it requires a fully quantum mechanical description of the system, $ extit{ab-initio}$ molecular dynamics is the ideal candidate to shed light on these processes. However, its scope is limited by a high computational cost. A popular alternative is to perform molecular dynamics simulations powered by machine learning potentials, trained on an extensive set of quantum mechanical calculations. Doing so reliably for reactive processes is difficult because it requires including very many intermediate and transition state configurations. In this study, we used an active learning procedure accelerated by enhanced sampling to harvest such structures and to build a neural-network potential to study the urea decomposition process in water. This allowed us to obtain the free energy profiles of this important reaction in a wide range of temperatures, to discover a number of novel metastable states and to improve the accuracy of the kinetic rates calculations. Furthermore, we found that the formation of the zwitterionic intermediate has the same probability of occurring via an acidic or a basic pathway, which could be the cause of the insensitivity of reaction rates to the pH solution.

研究动机与目标

• 开发一种可靠的机器学习势，用于模拟水相环境中的反应过程。
• 克服在复杂、涉及水的反应中从头算分子动力学的高计算成本。
• 实现水溶液中尿素分解的精确吉布斯自由能分布与动力学速率计算。
• 识别尿素分解机理中此前未知的亚稳态与反应路径。
• 通过分析反应路径，研究尿素分解动力学的pH依赖性。

提出的方法

• 采用元动力学方法增强尿素-水体系中罕见反应事件的采样。
• 利用主动学习迭代选择关键构型进行量子力学计算。
• 基于增强采样中获得的过渡态和中间体数据，训练神经网络势。
• 将训练好的神经网络势与吉布斯自由能计算相结合，绘制势能面。
• 在不同温度下使用神经网络势进行分子动力学模拟。
• 应用偏差交换元动力学方法，沿反应坐标计算吉布斯自由能分布。

实验结果

研究问题

• RQ1 尿素在水溶液中的完整吉布斯自由能分布在不同温度下如何？
• RQ2 尿素分解路径上存在哪些新型亚稳态？
• RQ3 酸性与碱性路径对两性离子中间体形成的贡献如何？
• RQ4 为何尿素分解反应速率在水溶液中对pH不敏感？
• RQ5 通过主动学习与增强采样训练的机器学习势能否准确描述水中的复杂反应事件？

主要发现

• 本研究在尿素分解路径上识别出多个此前未被检测到的新型亚稳态。
• 利用神经网络势在一系列温度下计算了尿素分解的吉布斯自由能分布。
• 两性离子中间体的形成在酸性与碱性路径下具有相近的概率。
• 反应速率对pH不敏感的原因在于酸性与碱性路径在能量上具有相近的可及性。
• 主动学习与元动力学方法显著提升了神经网络势的采样效率与准确性。
• 神经网络势使此前标准采样方法难以实现的可靠动力学速率计算成为可能。