Machine learning molecular dynamics of 8000-atom Si-C-N-H systems reveals progressive carbon phase separation into graphene-like domains with 5- and 7-membered rings mediating the formation of stable 6-membered aromatic structures.
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Modeling phase separation in polymer-derived carbonitride ceramics through extended machine learning molecular dynamics
Machine learning molecular dynamics of 8000-atom Si-C-N-H systems reveals progressive carbon phase separation into graphene-like domains with 5- and 7-membered rings mediating the formation of stable 6-membered aromatic structures.