Nitrogen atom‐rich heterocycles and organic azides have found extensive use in many sectors of modern chemistry from drug discovery to energetic materials. The prediction and understanding of their energetic properties are thus key to the safe and effective application of these compounds. In this work, we disclose the use of multivariate linear regression modeling for the prediction of the decomposition temperature and impact sensitivity of structurally diverse tetrazoles and organic azides. We report a data‐driven approach for property prediction featuring a collection of quantum mechanical parameters and computational workflows. The statistical models reported herein carry predictive accuracy as well as chemical interpretability. Model validation was successfully accomplished via tetrazole test sets with parameters generated exclusively in silico. Mechanistic analysis of the statistical models indicated distinct divergent pathways of thermal and impact‐initiated decomposition.
- Award ID(s):
- 1763436
- NSF-PAR ID:
- 10353884
- Date Published:
- Journal Name:
- ChemRxiv
- ISSN:
- 2573-2293
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Nitrogen atom‐rich heterocycles and organic azides have found extensive use in many sectors of modern chemistry from drug discovery to energetic materials. The prediction and understanding of their energetic properties are thus key to the safe and effective application of these compounds. In this work, we disclose the use of multivariate linear regression modeling for the prediction of the decomposition temperature and impact sensitivity of structurally diverse tetrazoles and organic azides. We report a data‐driven approach for property prediction featuring a collection of quantum mechanical parameters and computational workflows. The statistical models reported herein carry predictive accuracy as well as chemical interpretability. Model validation was successfully accomplished via tetrazole test sets with parameters generated exclusively in silico. Mechanistic analysis of the statistical models indicated distinct divergent pathways of thermal and impact‐initiated decomposition.
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Tetrazoles are well known for their high positive enthalpy of formation which makes them attractive as propellants, explosives, and energetic materials. As a step towards a deeper understanding of the stability of benziodazolotetrazole (BIAT)-based materials compared to their benziodoxole (BIO) counterparts, we investigated in this work electronic structure features and bonding properties of two monovalent iodine precursors: 2-iodobenzoic acid and 5-(2-iodophenyl)tetrazole and eight hypervalent iodine (III) compounds: I-hydroxybenzidoxolone, I-methoxybenziodoxolone, I-ethoxybenziodoxolone, I-iso-propoxybenziodoxolone and the corresponding I-hydroxyben ziodazolotetrazole, I-methoxybenziodazolotetrazole, I-ethoxybenziodazolotetrazole and I-iso- propoxybenziodazolotetrazole. As an efficient tool for the interpretation of the experimental IR spectra and for the quantitative assessment of the I−C, I−N, and I−O bond strengths in these compounds reflecting substituent effects, we used the local vibrational mode analysis, originally introduced by Konkoli and Cremer, complemented by electron density and natural bond orbital analyses. Based on the hypothesis that stronger bonds correlate with increased stability, we predict that, for both series, i.e., substituted benziodoxoles and benziodazolotetrazoles, the stability increases as follows: I-iso-propoxy < I-ethoxy < I-methoxy < I-hydroxy. In particular, the I−N bonds in the benziodazolotetrazoles could be identified as the so-called trigger bonds being responsible for the initiation of explosive decomposition in benziodazolotetrazoles. The new insight gained by this work will allow for the design of new benziodazolotetrazole materials with controlled performance or stability based on the modulation of the iodine bonds with its three ligands. The local mode analysis can serve as an effective tool to monitor the bond strengths, in particular to identify potential trigger bonds. We hope that this article will foster future collaboration between the experimental and computational community being engaged in vibrational spectroscopy.more » « less
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Abstract Objective This study aims to establish an informative dynamic prediction model of treatment outcomes using follow-up records of tuberculosis (TB) patients, which can timely detect cases when the current treatment plan may not be effective.
Materials and Methods We used 122 267 follow-up records from 17 958 new cases of pulmonary TB in the Republic of Moldova. A dynamic prediction framework integrating landmark modeling and machine learning algorithms was designed to predict patient outcomes during the course of treatment. Sensitivity and positive predictive value (PPV) were calculated to evaluate performance of the model at critical time points. New measures were defined to determine when follow-up laboratory tests should be conducted to obtain most informative results.
Results The random-forest algorithm performed better than support vector machine and penalized multinomial logistic regression models for predicting TB treatment outcomes. For all 3 outcome classes (ie, cured, not cured, and died after 24 months following treatment initiation), sensitivity and PPV of prediction models improved as more follow-up information was collected. Specifically, sensitivity and PPV increased from 0.55 to 0.84 and from 0.32 to 0.88, respectively, for the not cured class.
Conclusion The dynamic prediction framework utilizes longitudinal laboratory test results to predict patient outcomes at various landmarks. Sputum culture and smear results are among the important variables for prediction; however, the most recent sputum result is not always the most informative one. This framework can potentially facilitate a more effective treatment monitoring program and provide insights for policymakers toward improved guidelines on follow-up tests.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.26434/chemrxiv-2021-16f6w-v2
