Machine learning-driven atomistic simulation of mechanical deformation in nanoporous amorphous carbon

JH Yu and XX Zhao and KH Xun and SZ Zhang and L Jiang and J Ding, CARBON, 243, 120507 (2025).

DOI: 10.1016/j.carbon.2025.120507

Nanoporous amorphous carbon (NP alpha-C) is a promising material for next-generation energy storage systems, particularly as a key component in lithium-ion battery anodes. However, its disordered atomic structure and complex nanoscale porosity pose significant challenges for understanding its structure-property relationships. In this study, we generated and analyzed over 200,000 unique NP alpha-C configurations using the Gaussian Random Field Method combined with machine learning- driven molecular dynamics simulations. This approach enabled the creation of an extensive structural database, covering porosities from 10 % to 90 %, average pore sizes from 5 to 60 & Aring;, and pore size variances from 0 to 60 & Aring;2. Our findings reveal that pore structure plays a crucial role in governing the elastic and plastic behavior of NP alpha-C. Under triaxial tension, stress concentrates at ligamentjunction regions, leading to ligament thinning, single-chain formation, and eventual fracture. Cyclic loading tests further demonstrate that most fractures occur in the first cycle, with minimal crack propagation and a significant reduction in elastic constants in subsequent cycles. This study establishes a robust theoretical framework for optimizing NP alpha-C microstructures, offering valuable insights into the design of high-performance porous materials for energy storage applications.

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