Rich Dynamics Underlying Solution Reactions Revealed by Sampling and Data Mining of Reactive Trajectories

Abstract

Efficient sampling in both configuration and trajectory spaces, combined with mechanism analyses via data mining, allows a systematic investigation of the thermodynamics, kinetics, and molecular-detailed dynamics of chemical reactions in solution. Through a Bayesian learning algorithm, the reaction coordinate(s) of a (retro-)Claisen rearrangement in bulk water was variationally optimized. The bond formation/breakage was found to couple with intramolecular charge separation and dipole change, and significant dynamic solvent effects manifest, leading to the “in-water” acceleration of Claisen rearrangement. In addition, the vibrational modes of the reactant and the solvation states are significantly coupled to the reaction dynamics, leading to heterogeneous and oscillatory reaction paths. The calculated reaction rate is well interpreted by the Kramers’ theory with a diffusion term accounting for solvent–solute interactions. These findings demonstrated that the reaction mechanisms can be complicated in homogeneous solutions since the solvent–solute interactions can profoundly influence the reaction dynamics and the energy transfer process.

Deep Learning for Variational Multi-Scale Molecular Modeling

“…Fig. 5A), which involves relatively high energy barrier, in the media of ionic liquid (see more details about simulation setup in SI).According to previous studies(46), a linear combination of the breaking/forming bonds (i.e., 1 and 2 inFig. 5A) is selected as the CV , and the target distribution over takes a Lorentzian form(47) which is developed to help enhance the sampling of the transition state regions (see more details about the CV, target distribution and training details in SI).…”

The importance of dynamic effects in chemical reactions in condensed phase

“…所有结果均经过轨迹系综平 均 [55] (网络版彩图) 此处我们从各个体系中选择了一条成功的转化轨 迹来展示结果 [55] . 对于水溶液, 溶质的能量(0~2000 fs) [42,48,55] , 集中讨论了两种效应: (1) 溶质溶剂间的耦合作用能影 响反应势垒穿越过程;…”

“…每 条轨迹都是以红色区域为起始态, 然后经过绿色的转换路 径区域到达蓝色的终态. 成功发射的轨迹的初始态概率 (−lnP sus )分布用热图进行表示, 颜色尺度如右上角所示 [42] (网 络版彩图) 而 改 变 势 能 面 形 状 实 际 上 就 可 以 改 变 其 反 应 机 理 [49,50] , 这导致两类轨迹机理的差异. [48] .…”

“…Every trajectory starts from red-colored basin, passing by the green-colored transition-path region and arrives at the blue-colored basin. The probability distribution (−lnP sus ) of initial configurations for successful shootings is shown in a heat map, rendered according to the upper-right color scale[42] (color online).而在克莱森重排反应中, 电子主要从C12经过O15流向C1(图4(c, d)), 由于该过程容易受到溶剂静电作用 的影响, 从而强的氢键会阻碍电子从O15流向C1(图4 (d)), 从而形成一个势阱. 两类轨迹形成了不同的溶剂 化结构(氢键), 使得两类轨迹长度出现了较大的差异;…”

ACS Central Science Virtual Issue on Machine Learning

“… 13 The problem of drug design is engaged by Waller and co-workers employing recurrent neural networks as generative models, 14 by Aspuru-Guzik and co-workers using encoder-decoder network architectures, 15 and by Pande and co-workers using a novel network architecture to perform one-shot learning. 16 Yang and Gao and co-workers employ Bayesian learning and variational optimization to determine the reaction coordinate for an in-water (retro-)Claisen rearrangement, 17 Pentelute and co-workers use random forest classifiers to predict cell-penetrating peptides to deliver therapeutics, 18 and Aspuru-Guzik and co-workers apply automatic differentiation to compute derivatives in quantum chemical calculations. 19 In Center Stage , Neil Savage interviews Alán Aspuru-Guzik about quantum computing, machine learning, and open access.…”