arXiv subjects

Davis Unruh

Publications and source records attributed to Davis Unruh.

6 recordsLinked to original sources

arXiv2023

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

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.

cond-mat.mtrl-sci↗

arXiv2022

A Gaussian Approximation Potential for Amorphous Si:H

Hydrogenation of amorphous silicon (a-Si:H) is critical for reducing defect densities, passivating mid-gap states and surfaces, and improving photoconductivity in silicon-based electro-optical devices. Modelling 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 heterjunctions that are at the heart of the modern solar cells with world record efficiency. Density functional theory (DFT) studies achieve the required high accuracy but are limited to moderate system sizes a hundred atoms or so by their high computational cost. Simulations of amorphous materials in particular 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 modelled. 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. 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, with both DFT and experiments.

cond-mat.mtrl-sci↗

arXiv2021

Describing transport in defected nanoparticle solids using a new, hierarchical, simulation tool, TRIDENS

The efficiency of nanoparticle (NP) solar cells has grown impressively in recent years, exceeding 16%. However, the carrier mobility in NP solar cells, and in other optoelectronic applications remains low, thus critically limiting their performance. Therefore, carrier transport in NP solids needs to be better understood to further improve the overall efficiency of NP solar cell technology. However, it is technically challenging to simulate experimental scale samples, as physical processes from atomic to mesoscopic scales all crucially impact transport. To rise to this challenge, here we report the development of TRIDENS: the Transport in Defected Nanoparticle Solids Simulator, that adds three more hierarchical layers to our previously developed HINTS code for nanoparticle solar cells. In TRIDENS, we first introduced planar defects, such as twin planes and grain boundaries into individual NP SLs that comprised the order of 10^3 NPs. Then we used HINTS to simulate the transport across tens of thousands of defected NP SLs, and constructed the distribution of the NP SL mobilities with planar defects. Second, the defected NP SLs were assembled into a resistor network with more than 10^4 NP SLs, thus representing about 10^7 individual NPs. Finally, the TRIDENS results were analyzed by finite size scaling to explore whether the percolation transition, separating the phase where the low mobility defected NP SLs percolate, from the phase where the high mobility undefected NP SLs percolate drives a low-mobility-to-high-mobility transport crossover that can be extrapolated to macroscopic length scales. For the theoretical description, we adapted the Efros-Shklovskii bimodal mobility distribution percolation model. We demonstrated that the ES bimodal theory's two-variable scaling function is an effective tool to quantitatively characterize this low-mobility-to-high-mobility transport crossover.

cond-mat.mes-hall↗

arXiv2021

From Femtoseconds to Gigaseconds: The SolDeg Platform for the Performance Degradation Analysis of Silicon Heterojunction Solar Cells

Heterojunction Si solar cells exhibit notable performance degradation. We developed the SolDeg platform to model this degradation as electronic defects getting generated by thermal activation across energy barriers over time. First, molecular dynamics simulations were performed to create a-Si/c-Si stacks, using a machine-learning-based Gaussian approximation potential. Second, we created shocked clusters by a cluster blaster. Third, the shocked clusters were analyzed to identify which of them supported electronic defects. Fourth, the distribution of energy barriers that control the generation of these electronic defects was determined. Fifth, an accelerated Monte Carlo method was developed to simulate the thermally activated time dependent defect generation across the barriers. Our main conclusions are as follows. (1) The degradation of a-Si/c-Si stacks via defect generation is controlled by a broad distribution of energy barriers. (2) We developed the SolDeg platform to track the microscopic dynamics of defect generation across this wide barrier distribution, and determined the time dependent defect density $N(t)$ from femtoseconds to gigaseconds, over 24 orders of magnitude in time. (3) We have shown that a stretched exponential analytical form can successfully describe the defect generation $N(t)$. (4) We found that in relative terms $V_\mathrm{oc}$ degrades at a rate of 0.2%/year over the first year, slowing with advancing time. (5) We developed the Time Correspondence Curve to calibrate and validate the accelerated testing of solar cells. We found a compellingly simple scaling relationship between accelerated and normal times $t_\mathrm{accelerated} \propto t_\mathrm{normal}^{0.85}$. (6) We ourselves carried out experimental studies of defect generation in a-Si:H/c-Si stacks. We found a relatively high degradation rate at early times, that slowed considerably at longer time scales.

cond-mat.mtrl-sci↗

arXiv2020

Percolative Charge Transport In Binary Nanocrystal Solids

We simulated electron transport across a binary nanocrystal solid (BNS) of PbSe NCs with diameters of 6.5nm and 5.1nm. We used our Hierarchical Nanoparticle Transport Simulator HINTS to model the transport in these BNSs. The mobility exhibits a minimum at a Large-NC-fraction f_LNC=0.25. The mobility minimum is deep at T=80K and partially smoothed at T=300K. We explain this minimum as follows. As the LNC fraction f_LNC starts growing from zero, the few LNCs act as traps for the electrons traversing the BNS because their relevant energy level is lower. Therefore, increasing the f_LNC concentration of these traps decreases the mobility. As increasing f_LNC reaches the percolation threshold f_LNC=f_p, the LNCs form sample-spanning networks that enable electrons to traverse the entire BNS via these percolating LNC networks. Transport through the growing percolating LNC networks drives the rapid growth of the mobility as f_LNC grows past f_p. Therefore, the electron mobility exhibits a pronounced minimum as a function of f_LNC, centered at f_LNC=f_p. The position of the mobility minimum shifts to larger LNC fractions as the electron density increases. We have studied the trends of this mobility minimum with temperature, electron density, charging energy, ligand length, and disorder. We account for the trends by a "renormalized trap model", in which capturing an electron renormalizes a deep LNC trap into a shallow trap or a kinetic obstacle, depending on the charging energy. We verified this physical picture by constructing and analyzing heat maps of the mobile electrons in the BNS.

cond-mat.mes-hall↗

arXiv2020

Disordered Mott-Hubbard Physics in Nanoparticle Solids: Persistent Gap Across the Disorder-localized-to-Mott-localized Transition

We show that Nanoparticle (NP) solids are an exciting platform to seek new insights into the disordered Mott-Hubbard physics. We developed a "Hierarchical Nanoparticle Transport Simulator" (HINTS), which builds from localized states to describe the Disorder-localized and Mott-localized phases, and the transitions out of these localized phases. We also studied the interplay between correlations and disorder in the corresponding multi-orbital Hubbard model at and away from integer filling by Dynamical Mean Field Theory. This approach is complementary to HINTS, as it builds from the metallic phase of the NP solid. The mobility scenarios and phase diagrams produced by the two methods are strikingly similar, and account for the mobilities measured in NP solids.

cond-mat.mes-hall↗