Sogabe TOHMA (曽我部 東馬)

Self-consistent drift–diffusion model has been widely employed to simulate the device performance of intermediate band solar cell (IBSC) under practical device configuration. However, one of the remained issues in the drift–diffusion modeled-based works is the difficulty to reach the IB carrier continuity through the self-consistent manner. In most of the previous reports the constraints were relaxed or just partially satisfied; which render the unreliable performance results and misguide the device design strategy. In this work, in order to solve this issue and to validate our results, we performed extensive simulations to fully disclose the significant effect of the IB continuity constraints by taking InAs/GaAs quantum dot-based IBSC as a model device using the semiconductor modules in COMSOL Multiphysics combined with the Fortran codes. We found that under rigorous satisfaction of IB continuity constraint, the band potential profiles for the IBSC with either doped or nondoped IB under various light illumination conditions are nearly identical to those under the dark conditions. Moreover, from the simulated current–voltage curve dependence on the light concentration ratio, we found the device performance based on drift–diffusion under rigorous IB continuity constraint showed similar tendency to the features simulated based on detailed balance principle except the much-lowered power conversion efficiency. Our work demonstrated here, serves as an accurate and reliable IBSC device design approach toward better IB material screening, efficiency improvement, optical management, and extended application in the emerging field such as the perovskite material-based IBSC.

Recent experimental results suggest that higher mobility of perovskite-type ligand passivated PbS quantum dots (QDs) could be useful for efficient solar cell applications. However, theoretical understanding of the mechanism through first principal modeling is still lacking. In this study, electronic-, optical-, and temperature-dependent carrier mobility for perovskite ligand passivated PbS QD array is calculated by using the first-principles density functional theory (DFT) combined with the nonequilibrium Green’s function (NEGF) technique and a molecular dynamics (MD)-Landauer approach. It is found that formamidinium (FA)-liganded QDs have higher mobility and enhanced optical absorption comparing to that of Cl-liganded QDs. The difference could be understood through the intermediate band featured electronic structure.

A deep understanding of the effect of the A-site cation cross-exchange on the hot-carrier relaxation dynamics in perovskite quantum dots (PQDs) has profound implications on the further development of disruptive photovoltaic technologies. In this study, we investigated the hot carrier cooling kinetics of pure FAPbI3 , MAPbI3 , CsPbI3 and alloyed FA0.5 MA0.5 PbI3 , FA0.5 Cs0.5 PbI3 , and MA0.5 Cs0.5 PbI3 QDs using ultrafast transient absorption (TA) spectroscopy. The lifetimes of the initial fast cooling stage (<1 ps) of all the organic cation-containing PQDs were shorter than those of the CsPbI3 QDs, as verified by the electron-phonon coupling strength extracted from the temperature-dependent photoluminescence spectra. The lifetimes of the slow cooling stage of the alloyed PQDs were longer under illumination greater than 1 sun, which was ascribed to the introduction of co-vibrational optical phonon modes in the alloyed PQDs. This facilitated efficient acoustic phonon upconversion and enhanced the hot-phonon bottleneck effect, as demonstrated by first-principles calculations. This article is protected by copyright. All rights reserved.

Here, we report a case study on inverse design of quantum dot optical spectra using a deep reinforcement learning algorithm for the desired target optical property of semiconductor Cd _xSe _yTe _{x− y} quantum dots. Machine learning models were trained to predict the optical absorption and emission spectra by using the training dataset by time dependent density functional theory simulation. We show that the trained deep deterministic policy gradient inverse design agent can infer the molecular structure with an accuracy of less than 1 Å at a fixed computational time of milliseconds and up to 100–1000 times faster than the conventional heuristic particle swam optimization method. Most of the effective inverse design problems based on the surrogate machine learning and reinforcement learning model have been focused on the field of nano-photonics. Few attempts have been made in the field of quantum optical system in a similar manner. For the first time, our results, to our knowledge, provide concrete evidence that for computationally challenging tasks, a well-trained deep reinforcement learning agent can replace the existing quantum simulation and heuristics optimization tool, enabling fast and scalable simulations of the optical property of nanometer sized semiconductor quantum dots.