论文标题
使用机器学习高斯近似电势模拟多壳富勒烯
Simulation of multi-shell fullerenes using Machine-Learning Gaussian Approximation Potential
论文作者
论文摘要
模拟了多壳富勒烯“ Buckyonions”，从最初的随机配置开始，使用密度功能理论（DFT）训练的机器学习碳电位（ML-GAP）框架[Volker L. Derker L. Deringer和Gabor Csananyi，Phys Phys，Phys。 Rev. B 95，094203（2017）]。获得了大量富勒烯，尺寸为60〜3774个原子。 Buckyonions是通过聚类形成的，分层是从最外层的外壳开始的，然后向内进行。壳间内聚力部分是由于Dellocalized $π$电子之间的相互作用在画廊中。使用密度函数代码VASP和SIESISESA在事后验证了模型的能量，揭示了在模型的Conjuagte梯度能量收敛之后，通过这两种方法实现了模型的0.02-0.08 eV/Atom的能量差。
Multi-shell fullerenes "buckyonions" were simulated, starting from initially random configurations, using a density-functional-theory (DFT)-trained machine-learning carbon potential within the Gaussian Approximation Potential (ML-GAP) Framework [Volker L. Deringer and Gabor Csanyi, Phys. Rev. B 95, 094203 (2017)]. A large set of such fullerenes were obtained with sizes ranging from 60 ~ 3774 atoms. The buckyonions are formed by clustering and layering starts from the outermost shell and proceed inward. Inter-shell cohesion is partly due to interaction between delocalized $π$ electrons into the gallery. The energies of the models were validated ex post facto using density functional codes, VASP and SIESTA, revealing an energy difference within the range of 0.02 - 0.08 eV/atom after conjuagte gradient energy convergence of the models were achieved with both methods.
