Dynamic Electric Field Complicates Chemical Reactions in Solutions

Generating High-Precision Force Fields for Molecular Dynamics Simulations to Study Chemical Reaction Mechanisms Using Molecular Configuration Transformer

Theoretical studies on chemical reaction mechanisms have been crucial in organic chemistry. Traditionally, calculating the manually constructed molecular conformations of transition states for chemical reactions using quantum chemical calculations is the most commonly used method. However, this way is heavily dependent on individual experience and chemical intuition. In our previous study, we proposed a research paradigm that used enhanced sampling in molecular dynamics simulations to study chemical reactions. This approach can directly simulate the entire process of a chemical reaction. However, the computational speed limited the use of high-precision potential energy functions for simulations. To address this issue, we presented a scheme for training high-precision force fields for molecular modeling using a previously developed graph-neural-network-based molecular model, molecular configuration transformer. This potential energy function allowed for highly accurate simulations at a low computational cost, leading to more precise calculations of the mechanism of chemical reactions. We applied this approach to study a Claisen rearrangement reaction and a carbonyl insertion reaction catalyzed by manganese.

Locating Transition Zone in Phase Space

Understanding the reaction mechanism is required for better control of chemical reactions and is usually achieved by locating transition states (TSs) along a proper one-dimensional coordinate called reaction coordinate (RC). The identification of RC can be very difficult for high-dimensional realistic systems. A number of methods have been proposed to tackle this problem. A machine learning method is developed here to incorporate the influence of velocity on the reaction process. The method is also free of the unbalanced label problem resulting from the rather low fraction of configurations near the TS and can be easily extended to large systems. It locates the transition zone in the phase space and defines the dividing surface with a high transmission coefficient. Moreover, considering that the reaction environment can not only change the reaction path but also activate the reactive mode through energy transfer, we devise two measures to quantify the influence of these two factors on the reaction process and find that solvents can assist the reaction by directly doing work along the reactive mode. Not surprisingly, there is a positive correlation between the efficiency of energy transfer into the reactive mode and the reaction rate.

Emergence of Electric Fields at the Water-C12E6 Surfactant Interface

We study the properties of the interface of water and the surfactant hexaethylene glycol monododecyl ether (C12E6) with a combination of heterodyne-detected vibrational sum frequency generation (HD-VSFG), Kelvin-probe measurements, and molecular dynamics (MD) simulations. We observe that the addition of the hydrogen-bonding surfactant C12E6, close to the critical micelle concentration (CMC), induces a drastic enhancement in the hydrogen bond strength of the water molecules close to the interface, as well as a flip in their net orientation. The mutual orientation of the water and C12E6 molecules leads to the emergence of a broad (∼3 nm) interface with a large electric field of ∼1 V/nm, as evidenced by the Kelvin-probe measurements and MD simulations. Our findings may open the door for the design of novel electric-field-tuned catalytic and light-harvesting systems anchored at the water-surfactant-air interface.

A robust immobilization strategy in the nano-dispersed Ziegler–Natta catalyst: non-covalent O–Ti coordination

A robust non-covalent O–Ti bond facilitates the synthesis of polyethylene with a narrow distribution.

Enhanced sampling in molecular dynamics

Although molecular dynamics simulations have become a useful tool in essentially all fields of chemistry, condensed matter physics, materials science, and biology, there is still a large gap between the time scale which can be reached in molecular dynamics simulations and that observed in experiments. To address the problem, many enhanced sampling methods were introduced, which effectively extend the time scale being approached in simulations. In this perspective, we review a variety of enhanced sampling methods. We first discuss collective-variables-based methods including metadynamics and variationally enhanced sampling. Then, collective variable free methods such as parallel tempering and integrated tempering methods are presented. At last, we conclude with a brief introduction of some newly developed combinatory methods. We summarize in this perspective not only the theoretical background and numerical implementation of these methods but also the new challenges and prospects in the field of the enhanced sampling.