Towards Transferable Machine Learning Interatomic Potentials for Reactive Organic Chemistry in Solution

液相中的反应是涉及合成有机化学的研究和工业的中心。溶剂以复杂的方式影响反应机理和速率，包括非共价相互作用和质子转移。溶剂效应尚未完全了解。计算机模拟可以通过自由能计算和在高理论水平上处理整个系统来帮助理解溶剂效应。这通常使用从头算分子动力学（AIMD）完成，由于计算成本高，其应用范围有限。机器学习原子间势（MLIP）正在成为精确凝聚相模拟的常用工具，与AIMD相比，扩展了模拟可访问的时间和长度范围，而不会显著损失准确性。制定多边投资政策的关键挑战是对相关生态系统服务费用进行有效采样。已经尝试构建一般ML势以及尝试创建用于特定凝聚相反应的MLIP。MLIP的高度灵活性为FF打开了大门，FF准确地代表了由选择的化学元素所跨越的大部分化学成分。还没有人试图建立一个通用的反应MLIP研究溶液中的有机反应。我们将开发一种策略，以有效地采样配置，代表一个选定的溶液相反应系统的PES。然后，我们将推广这种方法，以构建通用MLIP，该MLIP在不同的反应物-溶剂系统中实现合理的准确度，并且不需要为每个新应用重新训练。最后，我们希望使用我们的模型进行计算的反应系统相关的现代有机化学研究。

Reactions in liquid phase are central to research and industry involving synthetic organic chemistry. Solvents influence reaction mechanism and rate in complicated ways involving non-covalent interactions and proton transfers. Solvent effects are not yet completely understood. Computer simulation can aid the understanding of solvent effects through free energy calculations and treatment of the full system at a high level of theory. This is commonly done using ab initio molecular dynamics (AIMD), which has a limited scope of application due to high computational cost. Machine learning interatomic potentials (MLIPs) are becoming a common tool for accurate condensed phase simulation, extending the time- and length-scale accessible to simulation without significant loss of accuracy compared with AIMD. The key challenge to developing an MLIP is the efficient sampling of relevant PES. There have been attempts at building a general ML potential as well as attempts at creating MLIPs for a specific condensed phase reaction. The high flexibility of MLIPs opens the door to FFs that accurately represent a large fraction of the chemistry spanned by a selection of chemical elements. There has not been attempt to build a general reactive MLIP for studying organic reactions in solution. We will develop a strategy to efficiently sample configurations that represent the PES of a chosen solution-phase reactive system. We will then generalise this approach to build a general-purpose MLIP that achieves reasonable accuracy across different reactant-solvent systems and does not need to be re-trained for each new application. Finally, we hope to use our models to carry out calculations of reactive systems relevant to modern organic chemistry research.