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Machine Learning for Energetic Materials I

FOCUS · K02 · ID: 1459402

Presentations

  • “Research output software for energetic materials based on observational modelling/ machine learning” (RoseBoom<sup>©</sup>)

    ORAL

    –

    Publication: Klapötke, T. M., Wahler, S., Zeitschrift für Naturforsch. B 2021, 76, 341–344. <br><br>Parisi, E., Landi, A., Fusco, S., Manfredi, C., Peluso, A., Wahler, S., Klapötke, T. M., Centore, R., Inorg. Chem. 2021, 60, 16213–16222. <br>Wahler, S., Klapötke, T. M., Research output software for energetic materials based on observational modelling 2.1 (RoseBoom2.1©), Mater. Adv. 2022, 3, 7976-7986<br><br>Wahler, S., Klapötke, T. M., Research output software for energetic materials based on observational modelling 2.2 (RoseBoom2.2©) – update to calculate the specific impulse, detonation velocity, detonation pressure and density for CHNO mixtures using the Supersloth-function, Cent. Eur. J. Energ. Mater, 2022, 19(3): 295-310, 2022<br><br>Wahler, S., Klapötke, T. M., RoseMortar-Equation – a universal equation to predict the strength of an explosive in a ballistic mortar test, , International Journal of Energetic Materials and Chemical Propulsion, 21(6):47–50 (2022) <br><br>Wahler, S., Klapötke, T. M., RoseTrauzl-Equation – a universal equation to predict the strength of an explosive in a Trauzl-test, 2022, Journal of Energetic Materials, accepted Manuscript, https://doi.org/10.1080/07370652.2022.2150793<br><br>Wahler, S.*, Klapötke, T. M., Chung, P., Training machine learning models based on the structural formula for the enthalpy of vaporization and sublimation and a thorough analysis of Trouton's rules., 2023, submitted Manuscript<br><br>Wahler, S.*, Klapötke, T. M., Chung, P., Improved density predictions based on the structural formula using empirical modelling and machine learning, 2023, submitted Manuscript<br><br>Wahler, S.*, Klapötke, T. M., Chung, P., A thorough study of Joback and Reid's Group additivity theory and new machine learning models to predict the enthalpy of formation of organic molecules, 2023, submitted Manuscript<br><br>Wahler, S., Klapötke, T. M., Proud, W.G., Testing open-source tools for optical chemical structure recognition on novel nitrogen-rich energetic materials, 2023, submitted Manuscript<br><br>Wahler, S., Klapötke, T. M., RoseSSRT-Equation – a simple linear correlation between the dent volume in a small scall-shock reactivity test and the heat of detonation, 2022, submitted Manuscript<br><br>Wahler, S., Klapötke, T. M., RoseDent-Equation – a simple linear correlation between the dent in the plate dent test and the detonation pressure and the reason we shouldn't rely too much on calculated detonation pressures, 2022, submitted Manuscript<br><br>Atceken, N., Wahler, S., Klapötke, T. M., A theoretical investigation of energetic abilities of nitrogen rich energetic materials in rocket propellant composites, 2022, submitted Manuscript<br><br>Wahler, S., Klapötke, T. M., Comparison of the implemented detonation velocity predictions in the Research output software for energetic materials based on observational modelling (RoseBoom©) to 31 experimental values, 2022, submitted Manuscript<br><br>Wahler, S., Klapötke, T. M., Representation of the seismic energy scale (RoseScale©), 2022, submitted Manuscript<br><br>Wahler, S., Klapötke, T. M., The predictions of RoseBoom2.2© without the input of any data received from experiments or composite methods, 2022, submitted Manuscript<br><br>Wahler, S., Klapötke, T. M., Comparison of the 478 specific impulses calculated with the ISPBKW code and 2 different empirical relationships encoded into RoseBoom©, submitted Manuscript

    Presenters

    • Sabrina Wahler

      LMU Munich, RoseExplosive UG

    Authors

    • Sabrina Wahler

      LMU Munich, RoseExplosive UG

    • William G Proud

      Imperial College London

    View abstract →

  • Group Equivalent Machine Learning Approach to Predict Hydrocarbon Strain Energy

    ORAL

    –

    Presenters

    • Jesse C Carter Hearn

      University of Maryland, College Park

    Authors

    • Jesse C Carter Hearn

      University of Maryland, College Park

    • Brian C Barnes

      U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory

    • Betsy M Rice

      US Army Research Lab Aberdeen

    • Peter W Chung

      University of Maryland, College Park

    View abstract →