成核
碳纤维
星团(航天器)
材料科学
航程(航空)
钻石
统计物理学
无定形碳
集群扩展
无定形固体
参数化复杂度
密度泛函理论
原子单位
算法
热力学
计算机科学
物理
计算化学
化学
量子力学
结晶学
复合材料
复合数
程序设计语言
作者
Minaam Qamar,Matous Mrovec,Yury Lysogorskiy,A. G. Bochkarev,Ralf Drautz
出处
期刊:Cornell University - arXiv
日期:2022-10-17
被引量:1
标识
DOI:10.48550/arxiv.2210.09161
摘要
We present an atomic cluster expansion (ACE) for carbon that improves over available classical and machine learning potentials. The ACE is parameterized from an exhaustive set of important carbon structures at extended volume and energy range, computed using density functional theory (DFT). Rigorous validation reveals that ACE predicts accurately a broad range of properties of both crystalline and amorphous carbon phases while being several orders of magnitude more computationally efficient than available machine learning models. We demonstrate the predictive power of ACE on three distinct applications, brittle crack propagation in diamond, evolution of amorphous carbon structures at different densities and quench rates and nucleation and growth of fullerene clusters under high pressure and temperature conditions.
科研通智能强力驱动
Strongly Powered by AbleSci AI