六方氮化硼在热和机械感应波纹中的机器学习潜力,The Journal of Physical Chemistry C

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六方氮化硼在热和机械感应波纹中的机器学习潜力
The Journal of Physical Chemistry C ( IF 3.3 ) Pub Date : 2020-09-14 , DOI: 10.1021/acs.jpcc.0c05831
Fabian L. Thiemann _{1,

2} , Patrick Rowe ₁ , Erich A. Müller ₃ , Angelos Michaelides _{1,

4}

Affiliation

我们基于高斯近似势（GAP）机器学习方法介绍六方氮化硼（hBN）的原子间势。潜力基于从密度泛函理论（DFT）模拟中收集的一组训练配置，并且能够处理块状和多层hBN以及任意手性的纳米管。发达的力场忠实地再现了DFT预测的势能面，同时将效率提高了几个数量级。我们通过比较地层能量，几何特性，声子色散谱和机械特性（相对于基准DFT计算和实验）来测试我们的潜力。此外，我们使用我们的模型和最近开发的石墨烯-GAP分析和比较大规模二维（2D）hBN和石墨烯中的热和机械引起的波纹。两种材料都表现出几乎相同的缩放行为，高度波动的指数η≈0.85与柔性膜的理论非常吻合。然而，基于其较低的抗弯曲性，hBN在零外加有限外力作用下会经历较大的面外偏差。压缩后，两种材料都观察到从非相干波纹运动到孤子波纹的相变。我们的潜力可从[http://www.libatoms.org]在线免费获得。85的高度波动与柔性膜的理论非常吻合。然而，基于其较低的抗弯曲性，hBN在零外加有限外力作用下会经历较大的面外偏差。压缩后，两种材料都观察到从非相干波纹运动到孤子波纹的相变。我们的潜力可从[http://www.libatoms.org]在线免费获得。85的高度波动与柔性膜的理论非常吻合。然而，基于其较低的抗弯曲性，hBN在零应变和有限外加应变下均会经历较大的面外偏差。压缩后，两种材料都观察到从非相干波纹运动到孤子波纹的相变。我们的潜力可从[http://www.libatoms.org]在线免费获得。

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Machine Learning Potential for Hexagonal Boron Nitride Applied to Thermally and Mechanically Induced Rippling

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 analyze and compare thermally and mechanically induced rippling in large scale two-dimensional (2D) hBN and graphene. Both materials show almost identical scaling behavior with an exponent of η ≈ 0.85 for the height fluctuations agreeing well with the theory of flexible membranes. On the basis of 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].

更新日期：2020-10-08

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