| Summary: | 碩士 === 國立臺灣大學 === 應用力學研究所 === 106 === Antimonene is a structure similar to graphene with twisted hexagonal rings and staggered antimony atoms with superior electronic properties over other two-dimensional materials as well as stability under ambient condition. However, fabrication of high-quality antimonene is difficult relative to other two-dimensional materials. One promising solution is to make use of layered transition-metal-dichalcogenide (TMD) material as growing template, and grow antimonene nanosheets atop, forming van der Waals heterostuctures. In this study, we explored the possibilities of utilizing molybdenum disulfide (MoS2) as the growing template of the vdW heterostructure by performing molecular simulations to examine the structural properties of antimonene grown on MoS2.
The most accurate measure of molecular simulation of antimonene/MoS2 heterostructures is ab initio molecular simulations. However, ab initio calculations are extremely computationally expensive, making them literally unusable for vdW heterostructures, which misfit strains or even twist angles must be taken into account. Classical molecular simulations can overcome system size issues. However, interatomic potential is the key component of classical molecular simulations, and it is extremely difficult to parameterize an interatomic potential for vdW heterostructures due to complex compositions and interactions. In this study, we harnessed the power of machine learning and constructed an artificial neural network (ANN) model to evaluate system energies with high fidelity to respective ab initio calculation for given structures. We anticipate that ANN model can have both of the computation efficiency of classical interatomic potential and the accuracy of the ab initio calculations. The ANN model was obtained by extensive training processes. In the training processes tens of thousands of structures of bulk antimonene, MoS2, and Sb/MoS2 heterostructures along with their energies from ab initio calculations were fed into the training sets. The trained ANN model can successfully evaluate energy of structures from both the training sets and validation sets with excellent agreements with those from ab initio calculations, suggesting that this trained ANN potential is robust and can be utilized for molecular simulations. We then performed classical molecular simulations of two different sb/MoS2 heterostructures with system size around one thousand atoms - a system size almost beyond the reach of ab initio calculations - using the trained ANN potential. Both systems were stable during the classical molecular simulations, and we also demonstrate that molecular simulations using the ANN potential yields much higher computation efficiency relative to ab initio molecular simulations. Next, we tested the ANN potential by performing structural optimization of Sb/MoS2 heterostructure by using both BFGS conjugate gradient minimizer, and we reveal the limitations of ANN potential model. Finally, we examined the optimized monolayer Sb/MoS2 heterostructure, and we observed distorted hexagonal rings in antimonene due to compressive misfit strains imposed on monolayer antimonene. The present study demonstrated that ANN model is a powerful tool in evaluating energies/forces of chemically complex systems such as vdW heterostructures with high accuracy, thereby allowing large scale molecular simulations for exploration of structural properties of vdW heterostructures.
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