About this Abstract

Meeting

MS&T24: Materials Science & Technology

Symposium

Machine Learning and Simulations

Presentation Title

New Machine–Learning Interatomic Potentials (MLIPs) for Si-C-O-H Compounds Enabling Atomistic Simulations of Complex Chemical Transformations

Author(s)

Mitchell Falgoust, Shariq Haseen, Peter Kroll

On-Site Speaker (Planned)

Mitchell Falgoust

Abstract Scope

We present new Machine–Learning Interatomic Potentials (MLIPs) for Si-C-O-H compounds, enabling simulations of structure, properties, and thermal conversion from polymer to ceramic. The MLIPs are constructed from Moment-Tensor-Potentials (MTPs) and trained to a library of configurations with added active and hybrid learning strategies. The MLIPs reproduce vibrational properties of polymers and SiCOH structures obtained from aiMD simulations, thus providing a tool to identify chemical units and distinct structural characteristics through their vibrational properties. Simulations of the polymer-to-ceramic transformation deliver details of chemical reaction mechanisms during the pyrolysis of polysiloxanes, including methane abstraction and Kumada-like rearrangements that transform the siloxane backbone. Simulations of millions of atoms for several nanoseconds show the development of mixed tetrahedra in SiCO ceramics and align with experimental observations. Moreover, they showcase precipitation of graphitic nanosheets from carbon-rich SiCO precursors. Overall, the new MLIPs deliver simulations for Si-C-O-H compounds with “DFT-like” quality at low and high temperatures.

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