Modeling the Argon bombardment and densification of Low-Temperature Organic Precursors using Reactive Molecular Dynamics Simulations and Machine Learning

In this study, we have systematically modeled the Argon bombardment and densification of the low-temperature processed organic precursors such as orthocarborane molecules to produce structural ceramics such as boron-carbide by using reactive Molecular Dynamics (MD) simulations. The simulations were employed to track and evaluate the interaction of Ar and the precursors to produce radicals and the densification processes of the aggregates generated from the bombardments. In addition, we identified and quantified the key chemical reactions associated with these processes by applying the Machine Learning (ML) algorithm into the database generated from the reactive MD simulations. The combined MD and ML approach has provided us with more insights into the overall mechanism. The support from the NSF-DMREF program (Award No. 1729176) is gratefully acknowledged.

Keywords: Orthocarboranes, Boron Carbide, Reactive MD Simulations, Data Mining, Machine Learning