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arXiv 2210.09161cond-mat.mtrl-sci
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用于碳的量子精度大规模模拟的原子团簇展开方法

Atomic cluster expansion for quantum-accurate large-scale simulations of carbon

Minaam Qamar, Matous Mrovec, Yury Lysogorskiy, Anton Bochkarev, Ralf Drautz

AI总结:

本研究提出一种碳的原子团簇展开(ACE)方法,基于DFT计算的碳结构数据集参数化,可高效准确预测多类碳相性质,并在裂纹扩展、非晶碳演化、富勒烯生长三类场景验证了有效性。

摘要:

我们提出了一种适用于碳的原子团簇展开(Atomic cluster expansion,ACE)方法,其性能优于现有的经典势和机器学习势。该ACE基于密度泛函理论(DFT)计算得到的、涵盖扩展体积与能量范围内重要碳结构的详尽数据集进行参数化。严格验证表明,ACE能准确预测晶态和非晶态碳相的广泛性质,同时计算效率比现有机器学习模型高几个数量级。我们在三类不同应用中展示了ACE的预测能力:金刚石中的脆性裂纹扩展、不同密度和淬火速率下非晶碳结构的演化,以及高压高温条件下富勒烯团簇的形核与生长。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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