Capturing dichotomic solvent behavior in solute–solvent reactions with neural network potentials

Simulations of chemical reactivity in condensed phase systems represent an ongoing challenge in computational chemistry, where traditional quantum chemical approaches typically struggle with both the size of the system and the potential complexity of the reaction. Here, we introduce a workflow aimed at efficiently training neural network potentials (NNPs) to explore energy barriers in solution at the hybrid density functional theory level. The computational burden associated with training at the PBE0-D3(BJ) level is bypassed through the use of active and transfer learning techniques, whereas extensive sampling of the transition state region is accelerated by well-tempered metadynamics simulations using multiple time-step integration. These NNPs serve to explore a puzzling solute--solvent reactivity route involving the ring opening of N-enoxyphthalimide experimentally observed in methanol but not in 2,2,2-trifluoroethanol (TFE). This reaction represents a challenging example characterized by intricate hydrogen bonding networks and structurally ambiguous solvent-sensitive transition states. The methodology successfully delivers detailed free energy surfaces and relative energy barriers in quantitative agreement with experiment. These barriers are associated with an ensemble of transition states involving direct participation of up to five solvent molecules. While this picture contrasts with the single transition state structure assumed by current static models, no drastic qualitative difference is observed between the formed hydrogen bonding networks and the number of participating solvent molecules in methanol or TFE. The dichotomy between the two solvents thus essentially arises from an electronic effect (i.e., distinct nucleophilicity) and from the larger conformational entropy contributions in methanol. This example underscores the critical role dynamic simulations at the ab initio levels play in capturing the full complexity of solute-solvent interactions.

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凝聚相系统中化学活性的模拟代表了计算化学领域的一项持续挑战，在此领域，传统的量子化学方法往往难以应对系统的规模以及反应可能带来的复杂性。在此，我们提出一种工作流程，旨在高效训练神经网络势能（NNPs），以在混合密度泛函理论级别上探索溶液中的能量势垒。通过运用主动学习和迁移学习技术，绕过了在PBE0-D3(BJ)级别进行训练所伴随的计算负担，而通过采用多步积分的微调元动力学模拟，加速了过渡态区域的广泛采样。这些NNPs旨在探索一个令人费解的溶质-溶剂反应路径，该路径涉及N-环氧苯甲酰亚胺的环开反应，这一反应在甲醇中观察到，但在2,2,2-三氟乙醇（TFE）中并未观察到。这一反应代表了一个具有复杂氢键网络和结构上模糊的溶剂敏感过渡态的挑战性例子。该方法成功提供了与实验定量一致的自由能表面和相对能量势垒。这些势垒与涉及直接参与多达五个溶剂分子的过渡态集合相关联。尽管这一图景与当前静态模型所假设的单个过渡态结构形成对比，但在甲醇或TFE中形成的氢键网络以及参与溶剂分子的数量之间并未观察到显著的定性差异。因此，两种溶剂之间的二分性实质上源于电子效应（即，不同的亲核性）以及在甲醇中的更大的构象熵贡献。这一例子强调了在原初级别上进行动态模拟在捕捉溶质-溶剂相互作用全复杂性方面所扮演的关键角色。

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