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 ...
Efficient sampling in both configuration and trajectory spaces, combined with mechanism analyses via...
Statistical rate theories such as transition state theory (TST) are central to the understanding of ...
Statistical rate theories such as transition state theory (TST) are central to the understanding of ...
In molecular simulations, the identification of suitable reaction coordinates is central to both the...
Understanding mechanistic aspects of reactivity lies at the heart of chemistry. Once the potential e...
In molecular simulations, the identification of suitable reaction coordinates is central to both the...
The mechanism of transition (reaction coordinate) during an activated process is best described in t...
In the quantum chemical approach, chemical reaction mechanisms are investigated based on a potential...
Abstract: Chemical compound space refers to the vast set of all possible chemical compounds, estimat...
We propose to analyze molecular dynamics (MD) output via a supervised machine learning (ML) algorith...
The interplay of kinetics and thermodynamics governs reactive processes, and their control is key in...
We propose to analyze molecular dynamics (MD) output via a supervised machine learning (ML) algorith...
Efficient sampling in both configuration and trajectory spaces, combined with mechanism analyses via...
A valuable aspect of reactive MD is to provide rate constants of complex reactions. We give some exa...
We present a systematic approach to reduce the dimensionality of a complex molecular system. Startin...
Efficient sampling in both configuration and trajectory spaces, combined with mechanism analyses via...
Statistical rate theories such as transition state theory (TST) are central to the understanding of ...
Statistical rate theories such as transition state theory (TST) are central to the understanding of ...
In molecular simulations, the identification of suitable reaction coordinates is central to both the...
Understanding mechanistic aspects of reactivity lies at the heart of chemistry. Once the potential e...
In molecular simulations, the identification of suitable reaction coordinates is central to both the...
The mechanism of transition (reaction coordinate) during an activated process is best described in t...
In the quantum chemical approach, chemical reaction mechanisms are investigated based on a potential...
Abstract: Chemical compound space refers to the vast set of all possible chemical compounds, estimat...
We propose to analyze molecular dynamics (MD) output via a supervised machine learning (ML) algorith...
The interplay of kinetics and thermodynamics governs reactive processes, and their control is key in...
We propose to analyze molecular dynamics (MD) output via a supervised machine learning (ML) algorith...
Efficient sampling in both configuration and trajectory spaces, combined with mechanism analyses via...
A valuable aspect of reactive MD is to provide rate constants of complex reactions. We give some exa...
We present a systematic approach to reduce the dimensionality of a complex molecular system. Startin...
Efficient sampling in both configuration and trajectory spaces, combined with mechanism analyses via...
Statistical rate theories such as transition state theory (TST) are central to the understanding of ...
Statistical rate theories such as transition state theory (TST) are central to the understanding of ...