Ab Initio Molecular Dynamics Simulations for Organic Chemists─It is About Time!

Molecular dynamics simulations model chemical reactions as continuous changes in molecular structure over time instead of static minima and transition states. This perspective argues that time-dependent structural change is a crucial, but often overlooked, mechanistic feature as many reactions simply do not follow a single, equilibrated minimum-energy path. We highlight examples where traditional transition state theory fails, typically cases involving short-lived intermediates, nonequilibrium solvation, momentum-controlled selectivity, post-transition state bifurcations, and "hidden" dynamic intermediates and show how molecular dynamics can reveal the actual sequence of structural change which governs a reaction outcome. We also discuss emerging machine learning-based molecular dynamics which have found applications in photochemistry and solvent modeling. While molecular dynamics will not replace methods based on transition state theory, it offers organic chemists a time-resolved view of molecular structure which can be crucial to understanding a given reaction. However, a central barrier for organic chemists is to understand when and why to apply an advanced computational technique such as molecular dynamics simulations. In this perspective, we aim to introduce the methodology in sufficient detail to enable organic chemists to make this assessment and gain an appreciation for the importance of time in reaction mechanisms.

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Publication Details

Journal
Journal of the American Chemical Society
Published
2026-04-15
DOI
https://doi.org/10.1021/jacs.5c20878
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

Ab Initio Molecular Dynamics Simulations for Organic Chemists─It is About Time!

Eric N. Jacobsen, M C Nielsen, Dean J.Tantillo, Corin Wagen et al.
Journal of the American Chemical Society
Machine Learning in Materials Science
article

Ab Initio Molecular Dynamics Simulations for Organic Chemists─It is About Time!

Eric N. Jacobsen, M C Nielsen, Dean J.Tantillo, Corin Wagen, Steven A. Lopez, Leticia A. Gomes
article en

Abstract

Molecular dynamics simulations model chemical reactions as continuous changes in molecular structure over time instead of static minima and transition states. This perspective argues that time-dependent structural change is a crucial, but often overlooked, mechanistic feature as many reactions simply do not follow a single, equilibrated minimum-energy path. We highlight examples where traditional transition state theory fails, typically cases involving short-lived intermediates, nonequilibrium solvation, momentum-controlled selectivity, post-transition state bifurcations, and "hidden" dynamic intermediates and show how molecular dynamics can reveal the actual sequence of structural change which governs a reaction outcome. We also discuss emerging machine learning-based molecular dynamics which have found applications in photochemistry and solvent modeling. While molecular dynamics will not replace methods based on transition state theory, it offers organic chemists a time-resolved view of molecular structure which can be crucial to understanding a given reaction. However, a central barrier for organic chemists is to understand when and why to apply an advanced computational technique such as molecular dynamics simulations. In this perspective, we aim to introduce the methodology in sufficient detail to enable organic chemists to make this assessment and gain an appreciation for the importance of time in reaction mechanisms.

Journal of the American Chemical SocietyVol. 148(16)
Harvard University Press (US), University of California System (US), Universidad del Noreste (MX)
Danmarks Frie Forskningsfond, National Institute of General Medical Sciences, Division of Chemistry
Openalex Percentile: Top 23%
Machine Learning in Materials Science
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Ab Initio Molecular Dynamics Simulations for Organic Chemists─It is About Time! — Eric N. Jacobsen, M C Nielsen, et al. · Journal of the American Chemical Society (2026) | TGRS Research Map | TGRS