六方氮化硼应用于热致和机械致波纹的机器学习潜力
Machine Learning Potential for Hexagonal Boron Nitride Applied to Thermally and Mechanically Induced Rippling
摘要：
本文基于高斯近似势（Gaussian Approximation Potential，GAP）机器学习方法，引入了六方氮化硼（hBN）的原子间相互作用势。潜在的是基于训练集的配置收集从密度泛函理论（DFT）模拟，并能够处理散装和多层hBN以及纳米管的任意手性。开发的力场忠实地再现了由DFT预测的势能面，同时提高了几个数量级的效率。我们测试我们的潜力，通过比较形成能，几何性质，声子色散谱和力学性能与基准DFT计算和实验。此外，我们使用我们的模型和最近开发的石墨烯-GAP来分析和比较大规模二维（2D）hBN和石墨烯中的热和机械诱导的波纹。这两种材料显示出几乎相同的缩放行为的指数$η\approx 0.85$的高度波动同意与柔性膜的理论。然而，基于其较低的抗弯曲性，hBN在零和有限施加的外部应变下都经历稍大的面外偏差。压缩时，观察到从非相干波纹运动到孤子波纹的相变的两种材料。我们的潜力可在[http://www.libatoms.org]上免费获取。
英文摘要：
We introduce an interatomic potential for hexagonal boron nitride (hBN) based on the Gaussian approximation potential (GAP) machine learning methodology. The potential is based on a training set of configurations collected from density functional theory (DFT) simulations and is capable of treating bulk and multilayer hBN as well as nanotubes of arbitrary chirality. The developed force field faithfully reproduces the potential energy surface predicted by DFT while improving the efficiency by several orders of magnitude. We test our potential by comparing formation energies, geometrical properties, phonon dispersion spectra and mechanical properties with respect to benchmark DFT calculations and experiments. In addition, we use our model and a recently developed graphene-GAP to analyse and compare thermally and mechanically induced rippling in large scale two-dimensional (2D) hBN and graphene. Both materials show almost identical scaling behaviour with an exponent of $η\approx 0.85$ for the height fluctuations agreeing well with the theory of flexible membranes. Based on its lower resistance to bending, however, hBN experiences slightly larger out-of-plane deviations both at zero and finite applied external strain. Upon compression a phase transition from incoherent ripple motion to soliton-ripples is observed for both materials. Our potential is freely available online at [http://www.libatoms.org].
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。
