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Molecular dynamics simulations using machine learning potential for a-Si:H/c-Si interface: Effects of oxygen and hydrogen on interfacial defect states

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  • FOCUS ISSUE: Machine-learned Potentials in Materials Research
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Abstract

Molecular dynamics simulations of a-Si:H/c-Si models with and without an oxygen layer at the interface were performed using a machine learning potential (MLP) that was efficiently trained using an on-the-fly scheme with ab initio molecular dynamics. The relaxation processes up to 1 ns at 500 and 700 K were simulated using MLP, and snapshots were evaluated using ab initio calculations to examine the in-gap states that could significantly affect the solar cell performance. The results showed that oxygen atoms passivated surface dangling bonds on c-Si, but simultaneously generated strain-induced in-gap states at the Si–O/a-Si interface. The hydrogen atoms suppressed the recrystallization of a-Si, distributed in a-Si particularly at the Si–O/a-Si interface because of the repulsive potential of the Si–O layer and contributed to the reduction of the in-gap states. Our results support experimental observation where optimization of the a-Si:H/O/c-Si interface could improve the performance of solar cells.

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Acknowledgments

We thank Drs. Kazuhiro Gotoh, Noritaka Usami, Alex Kutana, and Pradeep Varadwaj for fruitful discussions. We also thank Ferenc Karsai and Georg Kresse for their support with the VASP-MLP code.

Funding

No funding was received for conducting this study.

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Authors and Affiliations

  1. Nagoya University, Furo-Cho, Chikusa-Ku, Nagoya, Japan

    Takayuki Semba, Ryosuke Jinnouchi & Ryoji Asahi

  2. North Carolina State University, Raleigh, NC, USA

    Jacob McKibbin

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  1. Takayuki Semba
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  2. Jacob McKibbin
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  3. Ryosuke Jinnouchi
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  4. Ryoji Asahi
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Contributions

Computations of MLP done by TS with technical support by RJ; geometric analysis done by TS and JM; electronic structure analyses by RA and RJ; supervision RA; all authors have read and agreed to the published version of the manuscript.

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Correspondence to Ryoji Asahi.

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Semba, T., McKibbin, J., Jinnouchi, R. et al. Molecular dynamics simulations using machine learning potential for a-Si:H/c-Si interface: Effects of oxygen and hydrogen on interfacial defect states. Journal of Materials Research 38, 5151–5160 (2023). https://doi.org/10.1557/s43578-023-01155-x

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  • DOI: https://doi.org/10.1557/s43578-023-01155-x

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