[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122454-en":3,"doc-seo-122454-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122454,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Understanding impact sensitivity of energetic molecules by supervised machine learning","Machine learning models are developed to relate molecular structure to sensitivity to initiation by mechanical impact for a dataset of 485 energetic molecules. Models use features derived from SMILES strings to classify structures first by a binary split separating primary and secondary behavior, then by boundary divisions forming up to five impact sensitivity classes. The top accuracy reaches 0.79 with a random forest binary classifier; feature importance and SHAP indicate oxygen balance and molecular flexibility as key drivers, enabling interpretable design guidance and SMILES-based prediction.","Edinburgh Research Explorer  \nUnderstanding impact sensitivity of energetic molecules by supervised machine learning  \nCitation for published version:  \nQuayle, HM, Mohan, K, Seth, S, Pulham, CR & Morrison, CA 2025, 'Understanding impact sensitivity of energetic molecules by supervised machine learning', Digital Discovery.  \n[https://doi.org/10.1039/D5DD00357A](https://doi.org/10.1039/D5DD00357A)  \nDigital Object Identifier (DOI):  \n10.1039/D5DD00357A  \nLink:  \nLink to publication record in Edinburgh Research Explorer  \nDocument Version:  \nPublisher's PDF, also known as Version of record  \nPublished In:  \nDigital Discovery  \nGeneral rights  \nCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 24. Nov. 2025  \nOpen Access Article . Pu on 03 Octo 2025. Down on 10/14/2025blished ber loaded 12: 16:3 1 PM .  \nhicle is licensed under a Creative C mmons A 3 0 U d[ttr .](ttr .)ibution-NonCommercial npor e nce.  \nDigital  \nDiscovery  \nPAPER  \nView Article Online View Journal  \nCite this: DOI: 10 .1039/d5dd00357a  \nReceived 11th August 2025  \nAccepted 26th September 2025 DOI: 10.1039/d5dd00357a[rsc.li/digitaldiscovery](rsc.li/digitaldiscovery)  \nUnderstanding impact sensitivity of energetic molecules by supervised machine learning  \nHeather M. Quayle,  a Karthik Mohan, b Sohan Seth,b Colin R. Pulham  a and Carole A. Morrison  *a  \nMachine learning models have been developed to rationalise correlations between molecular structure and sensitivity to initiation by mechanical impact for a data set of 485 energetic molecules. The models use readily obtainable features derived from SMILES strings to classify structures, first by a binary split to differentiate between primary and secondary energetic material behaviour, and by subsequent boundary divisions to create up to five impact sensitivity classes. The best accuracy score was 0 .79, which was obtained for the binary classifier random forest model. Feature importance and SHAP analysis showed that the features most likely to categorise a molecule with a high impact sensitivity were a high oxygen balance and a high molecular flexibility. The outcome of this study gives easily interpretable information on how the structure of a molecule can be tailored to design energetic materials with desired impact sensitivity properties. Included model codes also allow users to predict the sensitivity classes of any additional molecular structures from a SMILES string.  \nIntroduction  \nEnergetic materials (EMs, explosives, propellants and pyrotechnics) are substances that contain large amounts of stored chemical energy that can be quickly released upon initiation.1 Their sensitivity to mechanical stimuli, i.e., the impact of a falling weight, can be quanti􀀁ed as impact sensitivity (IS), and is an important safety metric for EMs. For this reason, substantial efforts are devoted to measuring this property, which is typically achieved using a fall-hammer test,2 such asthe BAM apparatus.3 IS values are quoted as either an h50 value, which represents the height in centimetres at which a weight of known mass dropped onto the sample will induce initiation 50% of the time, or alternatively as an E50 value, where the data is recast in units of Joules. It is well known that this test comes with inherent subjectivity, as well as inconsistency, due to dependencies on variables such as cry","cbCairJy0tDREWBN","https://ap.wps.com/l/cbCairJy0tDREWBN","pdf",785097,1,11,"English","en",105,"# Introduction\n## Impact sensitivity measurement methods\n## Existing structure–sensitivity relationships\n## Machine learning strategy and dataset overview","[{\"question\":\"What is the goal of the supervised machine learning models in this study?\",\"answer\":\"To rationalise correlations between molecular structure and impact sensitivity to initiation by mechanical impact for a set of energetic molecules.\"},{\"question\":\"How do the models classify impact sensitivity?\",\"answer\":\"They build a feature-based classifier from SMILES-derived descriptors, starting with a binary split for primary vs secondary energetic material behavior, then applying boundary divisions to create up to five sensitivity classes.\"},{\"question\":\"Which molecular features are most associated with high impact sensitivity?\",\"answer\":\"Feature importance and SHAP analysis highlight high oxygen balance and high molecular flexibility as the most likely indicators for high impact sensitivity.\"}]","Understanding impact sensitivity of energetic molecules by supervised machine learning | 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is the goal of the supervised machine learning models in this study?","Question",{"text":75,"@type":76},"To rationalise correlations between molecular structure and impact sensitivity to initiation by mechanical impact for a set of energetic molecules.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the models classify impact sensitivity?",{"text":80,"@type":76},"They build a feature-based classifier from SMILES-derived descriptors, starting with a binary split for primary vs secondary energetic material behavior, then applying boundary divisions to create up to five sensitivity classes.",{"name":82,"@type":73,"acceptedAnswer":83},"Which molecular features are most associated with high impact sensitivity?",{"text":84,"@type":76},"Feature importance and SHAP analysis highlight high oxygen balance and high molecular flexibility as the most likely indicators for high impact 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