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Meidanshahi, Reza Vatan

Publications and source records attributed to Meidanshahi, Reza Vatan.

3 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Hydrogen-induced degradation dynamics in silicon heterojunction solar cells via machine learning

Abstract Among silicon-based solar cells, heterojunction cells hold the world efficiency record. However, their market acceptance is hindered by an initial 0.5% per year degradation of their open circuit voltage which doubles the overall cell degradation rate. Here, we study the performance degradation of crystalline-Si/amorphous-Si:H heterojunction stacks. First, we experimentally measure the interface defect density over a year, the primary driver of the degradation. Second, we develop SolDeg, a multiscale, hierarchical simulator to analyze this degradation by combining Machine Learning, Molecular Dynamics, Density Functional Theory, and Nudged Elastic Band methods with analytical modeling. We discover that the chemical potential for mobile hydrogen develops a gradient, forcing the hydrogen to drift from the interface, leaving behind recombination-active defects. We find quantitative correspondence between the calculated and experimentally determined defect generation dynamics. Finally, we propose a reversed Si-density gradient architecture for the amorphous-Si:H layer that promises to reduce the initial open circuit voltage degradation from 0.5% per year to 0.1% per year.

14 SOLAR ENERGY↗
DOE OSTI2022

Gaussian approximation potential for amorphous Si : H

Hydrogenation of amorphous silicon (a–Si : H) is critical for reducing defect densities, passivating midgap states and surfaces, and improving photoconductivity in silicon-based electro-optical devices. Modeling the atomic-scale structure of this material is critical to understanding these processes, which in turn is needed to describe c–Si/a–Si : H heterojunctions that are at the heart of modern solar cells with world-record efficiency. Density functional theory (DFT) studies achieve the required high accuracy but are limited to moderate system sizes of 100 atoms or so by their high computational cost. Simulations of amorphous materials have been hindered by this high cost because large structural models are required to capture the medium-range order that is characteristic of such materials. Empirical potential models are much faster, but their accuracy is not sufficient to correctly describe the frustrated local structure. Data-driven, machine-learned interatomic potentials have broken this impasse and have been highly successful in describing a variety of amorphous materials in their elemental phase. Here, we extend the Gaussian approximation potential (GAP) for silicon by incorporating the interaction with hydrogen, thereby significantly improving the degree of realism with which amorphous silicon can be modeled. We show that our Si : H GAP enables the simulation of hydrogenated silicon with an accuracy very close to DFT but with computational expense and run times reduced by several orders of magnitude for large structures. Here, we demonstrate the capabilities of the Si : H GAP by creating models of hydrogenated liquid and amorphous silicon and showing that their energies, forces, and stresses are in excellent agreement with DFT results, and their structure as captured by bond and angle distributions are in agreement with both DFT and experiments.

36 MATERIALS SCIENCE↗