Numerical study of anomalous dynamic scaling behaviour of (1+1)-dimensional Das Sarma-Tamborenea model

In order to discuss the finite-size effect and the anomalous dynamic scaling behaviour of Das Sarma-Tamborenea growth model,the (1+1)-dimensional Das Sarma-Tamborenea model is simulated on a large length scale by using the kinetic Monte-Carlo method.In the simulation,noise reduction technique is used in order to eliminate the crossover effect.Our results show that due to the existence of the finite-size effect,the effective global roughness exponent of the (1+1)-dimensional Das Sarma-Tamborenea model systematically decreases with system size L increasing when L > 256.This finding proves the conjecture by Aarao Reis[Aarao Reis F D A 2004 Phys.Rev.E 70 031607].In addition,our simulation results also show that the Das Sarma-Tamborenea model in 1+1 dimensions indeed exhibits intrinsic anomalous scaling behaviour.     

1+1维Wolf-Villain模型生长表面高度极值统计分布的数值模拟

为全面研究Wolf-Villain(WV)模型生长表面的统计性质,基于极值统计理论,模拟计算1+1维WV模型在饱和生长阶段表面的极大高度分布(maximal-height distribution,MAHD)和极小高度分布(minimal-height distribution,MIHD).结果表明,MAHD和MIHD在不同的系统尺寸下分别有较好的标度规律,这两个分布之间存在不对称性.其中,MAHD遵循-种常见的极值分布,即广义的Fisher-Tippett-Gumbel(FTG)型分布;而MIHD可以用-个修正的Fisher-Tippett-Gumbel(MFTG)型分布来描述.     

1+1维含噪声Kuramoto-Sivashinsky方程表面宽度分布率的数值计算

通过对1+1维含噪声Kuramoto-Sivashinsky(KS)方程进行数值计算,得到其在饱和状态下的表面宽度分布率并与Kardar-Parisi-Zhang(KPZ)方程进行比较.结果表明,1+1维含噪声KS方程的表面宽度分布率标度函数受有限尺寸效应影响较小,并与KPZ方程具有相近的表面宽度分布率标度函数.     

Evidence for the anomalous scaling behaviour of the molecular-beam epitaxy growth equation

According to the scaling idea of local slope, we investigate numerically and analytically anomalous dynamic scaling behaviour of (1+1)-dimensional growth equation for molecular-beam epitaxy. The growth model includes the linear molecular-beam epitaxy (LMBE) and the nonlinear Lai--Das Sarma--Villain (LDV) equations. The anomalous scaling exponents in both the LMBE and the LDV equations are obtained, respectively. Numerical results are consistent with the corresponding analytical predictions.