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Simultaneously improving reaction coverage and computational cost in automated reaction prediction tasks

Nature Computational Science volume 1, pages 479–490 (2021)Cite this article

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A preprint version of the article is available at ChemRxiv.

Abstract

Automated reaction prediction has the potential to elucidate complex reaction networks for applications ranging from combustion to materials degradation, but computational cost and inconsistent reaction coverage are still obstacles to exploring deep reaction networks. Here we show that cost can be reduced and reaction coverage can be increased simultaneously by relatively straightforward modifications of the reaction enumeration, geometry initialization and transition state convergence algorithms that are common to many prediction methodologies. These components are implemented in the context of yet another reaction program (YARP), our reaction prediction package with which we report reaction discovery benchmarks for organic single-step reactions, thermal degradation of a γ-ketohydroperoxide, and competing ring-closures in a large organic molecule. Compared with recent benchmarks, YARP (re)discovers both established and unreported reaction pathways and products while simultaneously reducing the cost of reaction characterization by nearly 100-fold and increasing convergence of transition states. This combination of ultra-low cost and high reaction coverage creates opportunities to explore the reactivity of larger systems and more complex reaction networks for applications such as chemical degradation, where computational cost is a bottleneck.

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Fig. 1: Overview of the YARP methodology.
Fig. 2: Overview of YARP performance at predicting single-step organic reactions from the Zimmerman dataset.
Fig. 3: Characterization of sequential and concerted reaction mechanisms discovered by YARP.
Fig. 4: Overview of YARP performance on predicting unimolecular degradation of 3-hydroperoxypropanal.
Fig. 5: Five multistep reaction pathways identified by YARP that exhibit more than 20 kcal mol–1 reduction in activation energy compared with single-step reaction pathways.
Fig. 6: Characterizing competing Diels–Alder ring-closures for a ketothioester.

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Data availability

The authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files. Source data for Figs. 3–6 and Extended Data Figs. 2 and 3 are available in Source Data. Data referenced from other studies were scraped from the manuscripts or supporting information of the indicated publications, including the Zimmerman20 and KHP decomposition datasets30. Further raw data sources generated by this work are available at https://doi.org/10.6084/m9.figshare.14766624 (ref. 66), including raw output files and molecular geometries.

Code availability

The version of YARP used in this study and a guide to reproducing the results is available through GitHub under the GNU GPL-3.0 License (https://github.com/zhaoqy1996/YARP). The specific version of the package used to generate the results in the current study can be found at https://doi.org/10.5281/zenodo.4947195 (ref. 67).

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Acknowledgements

The work performed by Q.Z. and B.M.S was made possible by the Office of Naval Research (ONR) through support provided by the Energetic Materials Program (MURI grant no. N00014-21-1-2476, Program Manager: C. Stoltz). B.M.S also acknowledges partial support for this work from the Dreyfus Program for Machine Learning in the Chemical Sciences and Engineering and the Purdue Process Safety and Assurance Center. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, USA

    Qiyuan Zhao & Brett M. Savoie

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Contributions

Q.Z. and B.M.S conceived and designed the study. Q.Z developed tools, performed analysis and wrote the paper. B.M.S. oversaw the project and wrote the paper. All authors reviewed the final manuscript.

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Correspondence to Brett M. Savoie.

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Reviewer recognition statement Nature Computational Science thanks Cyrille Lavigne, Andreas Hansen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Handling editor: Jie Pan, in collaboration with the Nature Computational Science team.

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Supplementary information

Extended Data Fig. 1 Illustration of Elementary Reaction Steps.

Two cases of the ‘break two bonds and form two bonds’ (b2f2) elementary reaction step (ERS). a, The two bonds involved in the ERS connect four different atoms. b, An atom is shared between the two bonds involved in the ERS.

Extended Data Fig. 2 Timing comparisons for YARP.

Wall times for reaction enumeration, GFN2-xTB/GSM, and Berny optimization with respect to the number of heavy atoms in the reactant. The cases shown here are drawn from the Zimmerman dataset. The computational cost of Berny optimization occupies 95% to 99% of the total cost while the GSM at most contributes ~ 5%. All walltimes are reported without parallelization (that is, single-core equivalent walltimes). Additional timing details are reported in Section 1 of the Supporting Information.

Source data

Extended Data Fig. 3 Comparison of b2f2 and b3f3 reaction searches and performance statistics.

Comparison of b2f2 and b3f3 reaction enumeration for the reactants 1,3-butadiene and ethene (17), and isobutene and water (13) from the Zimmerman dataset. a, Number of potential products, b, average number of DFT gradient calls per successful channel, c, the success rates of unique reactions and d, the intended rates of unique reactions. e, Five b3f3 reactions for 17 that exhibit lower activation barriers compared with the lowest barrier b2f2 reaction, including the Diels-Alder reaction (top). Activation energies are reported in kcal/mol and additional technical details for this comparison are reported in Section 2 of the Supporting Information.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–6, Tables 1–6 and discussion.

Source data

Source Data Fig. 2

Statistical source data for figure panels.

Source Data Fig. 3

Statistical source data for figure panels.

Source Data Fig. 4

Statistical source data for figure panels.

Source Data Fig. 5

Statistical source data for figure panels.

Source Data Fig. 6

Statistical source data for figure panels.

Source Data Extended Data Fig. 2

Statistical source data for figure panels.

Source Data Extended Data Fig. 3

Statistical source data for figure panels.

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Zhao, Q., Savoie, B.M. Simultaneously improving reaction coverage and computational cost in automated reaction prediction tasks. Nat Comput Sci 1, 479–490 (2021). https://doi.org/10.1038/s43588-021-00101-3

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