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Artificial Neural Network Model for Atomistic Simulations of \({\rm {Sb/MoS}_{2}}\) van der Waals Heterostructures

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Abstract

van der Waals (vdW) heterostructures have drawn significant amount of attentions because of their potential applications in the future electronic devices as well as quantum computing. Modeling the structural properties has been a challenging task due to the combinatory effects of both chemical complexity and spatial limitations of the first principle calculations. In this work, we trained an artificial neural network (ANN) model for atomistic simulations of \({\rm {Sb/MoS}}_{2}\) vdW heterostructures. The ANN model was trained from thousands of atomistic configurations along with energies computed from density functional theory (DFT) calculations. We demonstrated that the ANN model can successfully predict system energy with high fidelity with respect to DFT calculations with much less consumption of computational resources, manifesting that the ANN model is a powerful tool in atomistic simulations of chemically complex systems.

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Acknowledgements

We thank the Academia Sinica Career Development Award, Grant no. 2317-1050100, and Ministry of Science and Technology, Taiwan, Grant no. MOST 105-2112-M-001-009-MY3 for financial support, and the National Center for High-performance Computing for computational support.

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Authors and Affiliations

  1. Institute of Applied Mechanics, National Taiwan University, Taipei, Taiwan

    Yu-Xuan Wang & Chien-Cheng Chang

  2. Research Center for Applied Sciences, Academia Sinica, Taipei, Taiwan

    Hsin-An Chen & Chun-Wei Pao

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  1. Yu-Xuan Wang
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  2. Hsin-An Chen
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  4. Chien-Cheng Chang
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Corresponding authors

Correspondence to Chun-Wei Pao or Chien-Cheng Chang.

Appendix A: Parameters of Descriptor Functions

Appendix A: Parameters of Descriptor Functions

See Tables 2 and 3 in Appendix.

Table 2 Parameters of \(G^{II}\) descriptors for all elements in the present study
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Table 3 Parameters of \(G^{III}\) descriptors for all elements in the present study
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Wang, YX., Chen, HA., Pao, CW. et al. Artificial Neural Network Model for Atomistic Simulations of \({\rm {Sb/MoS}_{2}}\) van der Waals Heterostructures. Multiscale Sci. Eng. 1, 119–129 (2019). https://doi.org/10.1007/s42493-018-00004-y

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