(607b) Combining Atomistic Simulation and Machine Learning to Establish High Throughput Yet Tunable Synthesis Strategies for Carbon-Based Nanomaterials

Carbon nanoparticles (CNPs) are of tremendous interest for clean-energy technology due to the manifold of chemical, mechanical, electronic, and optoelectronic properties they can exhibit. However, practical application of these materials is hampered by current synthesis strategies. In particular, low-pressure techniques such as chemical vapor deposition and flame pyrolysis are well understood but relatively low throughput. Conversely, high-pressure methods like ultrasound cavitation and detonation can greatly enhance throughput (e.g., enabling rates of up to kgs/μs), but the underlying phenomena are not well understood due to the highly dynamic nature of these processes, hampering tunability.

In this presentation, we discuss recent collaborative efforts to bridge this gap through development of a new shockwave-based technique that enables rapid CNP synthesis and enhanced tunability over related high-pressure synthesis methods. A robust understanding of phenomena underlying material synthesis is critical for enabling tunability. Hence, we also discuss how we deploy ChIMES, our unique machine-learning-enhanced atomic-resolution simulation technique to simulate this synthesis process on scales overlapping with experiments. Ultimately, we show that our simulations provide a previously missing means of elucidating the complicated condensed-phase reaction driven phase separation and transformation processes that underly CNP shockwave synthesis.