Work

Graphically Defined Model Reactions Are Extensible, Accurate, and Systematically Improvable

Year: 2025

Type: article

Abstract: Achieving fast and accurate reaction prediction is central to a suite of chemical applications. Nevertheless, classic approaches based on templates or simple models are typically fast but with limited scope or accuracy, while the emerging machine learning-based models are limited in their transferability due to the lack of large reaction databases. Here, we address these limitations by formalizing the model reaction concept based on fixed-depth condensed reaction graphs that are shown to achieve a cost and accuracy balance that is applicable to many problems. The model reaction concept can be utilized to provide reliable predictions of activation energies and transition state geometries for a large range of organic reactions. In addition, using an alkane pyrolysis system as a benchmarking example, we show that the accuracy of the activation energy prediction can be further improved by adding correction terms based on the empirical Brønsted-Evans-Polanyi (BEP) relationship. These successful applications demonstrate that the model reaction can serve as a general tool to reduce the cost associated with ab initio transition state searches.

Source: Journal of Chemical Theory and Computation

DOI: 10.1021/acs.jctc.5c00279

Authors: Qiyuan Zhao, Veerupaksh Singla, Hsuan-Hao Hsu, Brett M. Savoie

Institutions: Davidson College, Purdue University West Lafayette, University of Notre Dame

Language: English

Topic: Advanced Chemical Physics Studies

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: Camille and Henry Dreyfus Foundation, Purdue University, Multidisciplinary University Research Initiative, Office of Naval Research

Awards: G3990976816, G562624926, G8876996369