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1. Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery

   https://doi.org/10.1039/d2sc05089g  

   Tu, Zhengkai; Stuyver, Thijs; Coley, Connor W. (January 2023, Chemical Science)

   The field of predictive chemistry relates to the development of models able to describe how molecules interact and react. It encompasses the long-standing task of computer-aided retrosynthesis, but is far more reaching and ambitious in its goals. In this review, we summarize several areas where predictive chemistry models hold the potential to accelerate the deployment, development, and discovery of organic reactions and advance synthetic chemistry. 

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2. Efficient exploration of reaction pathways using reaction databases and active learning

   https://doi.org/10.1063/5.0235715  

   Kuryla, Domantas; Csányi, Gábor; van_Duin, Adri_C T; Michaelides, Angelos (March 2025, The Journal of Chemical Physics)

   Lian, T (Ed.)

   The fast and accurate simulation of chemical reactions is a major goal of computational chemistry. Recently, the pursuit of this goal has been aided by machine learning interatomic potentials (MLIPs), which provide energies and forces at quantum mechanical accuracy but at a fraction of the cost of the reference quantum mechanical calculations. Assembling the training set of relevant configurations is key to building the MLIP. Here, we demonstrate two approaches to training reactive MLIPs based on reaction pathway information. One approach exploits reaction datasets containing reactant, product, and transition state structures. Using an SN2 reaction dataset, we accurately locate reaction pathways and transition state geometries of up to 170 unseen reactions. In another approach, which does not depend on data availability, we present an efficient active learning procedure that yields an accurate MLIP and converged minimum energy path given only the reaction end point structures, avoiding quantum mechanics driven reaction pathway search at any stage of training set construction. We demonstrate this procedure on an SN2 reaction in the gas phase and with a small number of solvating water molecules, predicting reaction barriers within 20 meV of the reference quantum chemistry method. We then apply the active learning procedure on a more complex reaction involving a nucleophilic aromatic substitution and proton transfer, comparing the results against the reactive ReaxFF force field. Our active learning procedure, in addition to rapidly finding reaction paths for individual reactions, provides an approach to building large reaction path databases for training transferable reactive machine learning potentials. 

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3. The Reaction between Mn and Se Layers: The Reaction between Mn and Se Layers

   https://doi.org/10.1002/zaac.201800378  

   Choffel, Marisa A.; Hamann, Danielle M.; Joke, Jordan A.; Cordova, Dmitri Leo; Johnson, David C. (December 2018, Zeitschrift für anorganische und allgemeine Chemie)
4. Statistics and Bias-Free Sampling of Reaction Mechanisms from Reaction Network Models

   https://doi.org/10.26434/chemrxiv-2023-wltcr-v2  

   Rappoport, Dmitrij (January 2023, The journal of physical chemistry A)

   Selection bias is inevitable in manually curated computational reaction databases but can have a significant impact on generalizability of quantum chemical methods and machine learning models derived from these data sets. Here, we propose quasireaction subgraphs as a discrete, graph-based representation of reaction mechanisms that has a well-defined associated probability space and admits a similarity function using graph kernels. Quasireaction subgraphs are thus well suited for constructing representative or diverse data sets of reactions. Quasireaction subgraphs are defined as subgraphs of a network of formal bond breaks and bond formations (transition network) composed of all shortest paths between reactant and product nodes. However, due to their purely geometric construction, they do not guarantee that the corresponding reaction mechanisms are thermodynamically and kinetically feasible. As a result, a binary classification of feasible (reaction subgraphs) and infeasible (non-reactive subgraphs) must be applied after sampling. In this paper, we describe the construction and properties of quasireaction subgraphs and characterize the statistics of quasireaction subgraphs from CHO transition networks with up to six nonhydrogen atoms. We explore their clustering using Weisfeiler–Lehman graph kernels. 

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5. Joining and decomposing reaction networks

   https://doi.org/10.1007/s00285-020-01477-y  

   Gross, Elizabeth; Harrington, Heather; Meshkat, Nicolette; Shiu, Anne (January 2020, Journal of Mathematical Biology)