论文标题
机器学习驱动的碳膜的模拟沉积：从低密度到钻石状的无定形碳
Machine learning driven simulated deposition of carbon films: from low-density to diamondlike amorphous carbon
论文作者
论文摘要
无定形碳（A-C）材料具有多种有趣且有用的特性，但是对它们的原子尺度结构的理解仍然不完整。在这里，我们报告了A-C膜沉积和生长的广泛原子模拟，并使用基于机器学习（ML）基于高斯的高斯近似电势（GAP）模型来描述原子间相互作用。我们广泛扩展我们的初始工作[Phys。莱特牧师。 120，166101（2018）]现在考虑到广泛的入射离子能量，因此对整个范围的样品进行建模，从低密度（$ sp^{2} $ - rich）到高密度（$ sp^{3} $ - 富含diamond-like like”）无晶形式的碳。在这些模拟中观察到了两种不同的机制，具体取决于影响能量：低能影响诱导$ sp $ - 和$ sp^{2} $ - 直接在影响部位周围占主导地位的增长，而高能量影响会导致果皮。此外，我们提出并应用了一个方案来计算A-C膜的各向异性弹性特性。我们的工作为这种有趣的无序固体以及一种概念性和方法论蓝图提供了基本的见解，用于模拟其他材料对其他材料的原子尺度沉积，并具有ML驱动的分子动力学。
Amorphous carbon (a-C) materials have diverse interesting and useful properties, but the understanding of their atomic-scale structures is still incomplete. Here, we report on extensive atomistic simulations of the deposition and growth of a-C films, describing interatomic interactions using a machine learning (ML) based Gaussian Approximation Potential (GAP) model. We expand widely on our initial work [Phys. Rev. Lett. 120, 166101 (2018)] by now considering a broad range of incident ion energies, thus modeling samples that span the entire range from low-density ($sp^{2}$-rich) to high-density ($sp^{3}$-rich, "diamond-like") amorphous forms of carbon. Two different mechanisms are observed in these simulations, depending on the impact energy: low-energy impacts induce $sp$- and $sp^{2}$-dominated growth directly around the impact site, whereas high-energy impacts induce peening. Furthermore, we propose and apply a scheme for computing the anisotropic elastic properties of the a-C films. Our work provides fundamental insight into this intriguing class of disordered solids, as well as a conceptual and methodological blueprint for simulating the atomic-scale deposition of other materials with ML-driven molecular dynamics.
