Machine Learning Models for High Explosive Crystal Density and Performance

Journal Article · 06 November 2024 · Chemistry of Materials

DOI: https://doi.org/10.1021/acs.chemmater.4c01978 · OSTI ID:2476700

Davis, Jack Vickery  ^[1]; Marrs, III, Frank Wayne  ^[1]; Cawkwell, Marc Jon  ^[1]; Manner, Virginia Warren  ^[1]

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1. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

The rate of discovery of new explosives with superior energy density and performance has largely stalled. Rapid property prediction through machine learning has the potential to accelerate the discovery of new molecules by screening of large numbers of molecules before they are ever synthesized. To support this goal, we assembled a 21,000-molecule database of experimentally synthesized molecules containing energetic functional groups. Using a combination of experimental density measurements and high throughput electronic structure and atomistic calculations, we calculated detonation velocities and pressures for all 21,000 compounds. Using these values, we trained machine learning models for the prediction of density, detonation velocity and detonation pressure. Notably, our model for crystal density surpassed the accuracy of all current models and decreased the root-mean square error (RMSE) of the previous best model by 20%. This improvement in model performance relative to past works is attributed to our handling of chiral-specified Simplified Molecular-Input Line-Entry System (SMILES) strings and introduction of a new molecular descriptor, MolDensity. To elucidate descriptor importance, we evaluated interpretable descriptors in terms of importance and compared the accuracy of a statistics-driven machine learning model against a model comprised of descriptors typically assumed to control material density. The inexpensive, yet highly accurate predictions from our models should enable creation of future artificial intelligence (AI) models that are able to screen large numbers (>10⁶) of compounds to find the highest performing compounds in terms of crystal density, detonation velocity and detonation pressure.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 2476700
• Report Number(s): LA-UR--24-26051
• Journal Information: Chemistry of Materials, Journal Name: Chemistry of Materials Journal Issue: 22 Vol. 36; ISSN 0897-4756
• Publisher: American Chemical Society (ACS)Copyright Statement
• Country of Publication: United States
• Language: English

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
crystals
explosives
machine learning
molecular modeling
molecules