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AIP_Conference_Proceedings__Applicability.pdf (404.65 kB)

Machine Learned Prediction of Reaction Template Applicability for Data-Driven Retrosynthetic Predictions of Energetic Materials

submitted on 30.03.2020, 12:41 and posted on 31.03.2020, 08:40 by Michael Fortunato, Connor W. Coley, Brian Barnes, Klavs F. Jensen
State of the art computer-aided synthesis planning models are naturally biased toward commonly reported chemical reactions, thus reducing the usefulness of those models for the unusual chemistry relevant to shock physics. To address this problem, a neural network was trained to recognize reaction template applicability for small organic molecules to supplement the rare reaction examples of relevance to energetic materials. The training data for the neural network was generated by brute force determination of template subgraph matching for product molecules from a database of reactions in U.S. patent literature. This data generation strategy successfully augmented the information about template applicability for rare reaction mechanisms in the reaction database. The increased ability to recognize rare reaction templates was demonstrated for reaction templates of interest for energetic material synthesis such as heterocycle ring formation.

The following article has been submitted to by the 21st Biennial APS Conference on Shock Compression of Condensed Matter. After it is published, it will be found at


MIT-ARL Cooperative Agreement Award W911NF-19-2-0034-P00001

OSD ARAP Project "Enhanced Energetic Effects"


Email Address of Submitting Author


Massachusetts Institute of Technology


United States

ORCID For Submitting Author


Declaration of Conflict of Interest

No conflict of interest