Reactive molecular dynamics simulations enable a detailed understanding of solvent effects on chemical reaction mechanisms and reaction rates. While classical molecular dynamics using reactive force fields allows significantly longer simulation time scales and larger system sizes compared with ab initio molecular dynamics, constructing reactive force fields is a difficult and complex task. In this work, we describe a general approach following the empirical valence bond framework for constructing ab initio reactive force fields for condensed phase simulations by combining physics-based methods with neural networks (PB/NNs). The physics-based terms ensure the correct asymptotic behavior of electrostatic, polarization, and dispersion interactions and are compatible with existing solvent force fields. NNs are utilized for a versatile description of short-range orbital interactions within the transition state region and accurate rendering of vibrational motion of the reacting complex. We demonstrate our methodology for a simple deprotonation reaction of the 1-ethyl-3-methylimidazolium cation with acetate to form 1-ethyl-3-methylimidazol-2-ylidene and acetic acid. Our PB/NN force field exhibits ∼1 kJ mol⁻¹ mean absolute error accuracy within the transition state region for the gas-phase complex. To characterize the solvent modulation of the reaction profile, we compute potentials of mean force for the gas-phase reaction as well as the reaction within a four-ion cluster and benchmark against ab initio molecular dynamics simulations. We find that the surrounding ionic environment significantly destabilizes the formation of the carbene product, and we show that this effect is accurately captured by the reactive force field. By construction, the PB/NN potential may be directly employed for simulations of other solvents/chemical environments without additional parameterization.

中文翻译：

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用于反应分子动力学的基于物理的神经网络力场：从 [EMIM+][OAc−] 研究卡宾的形成

反应性分子动力学模拟能够详细了解溶剂对化学反应机制和反应速率的影响。虽然与从头算分子动力学相比，使用反应力场的经典分子动力学允许显着更长的模拟时间尺度和更大的系统尺寸，但构建反应力场是一项艰巨而复杂的任务。在这项工作中，我们描述了一种遵循经验价键框架的通用方法，用于从头开始构建通过将基于物理的方法与神经网络 (PB/NNs) 相结合，用于凝聚相模拟的反作用力场。基于物理的术语确保静电、极化和色散相互作用的正确渐近行为，并与现有的溶剂力场兼容。NN 用于对过渡态区域内的短程轨道相互作用进行通用描述，并准确呈现反应复合物的振动运动。我们展示了我们的方法，用于 1-乙基-3-甲基咪唑鎓阳离子与乙酸盐的简单去质子化反应，形成 1-乙基-3-甲基咪唑-2-亚甲基和乙酸。我们的 PB/NN 力场表现出 ∼1 kJ mol ^-1气相复合物过渡态区域内的平均绝对误差精度。为了表征反应曲线的溶剂调制，我们计算了气相反应的平均力势以及四离子簇内的反应，并以从头算分子动力学模拟为基准。我们发现周围的离子环境显着破坏了卡宾产物的形成，并且我们表明这种效应被反应力场准确地捕获。通过构建，PB/NN 势可以直接用于模拟其他溶剂/化学环境，而无需额外的参数化。