Design of Organic-Inorganic Hybrid Heterostructured Semiconductors via High-Throughput Materials Screening for Optoelectronic Applications

Strategic Integration of Machine Learning in the Design of Excellent Hybrid Perovskite Solar Cells

The photoelectric conversion efficiency (PCE) of perovskites remains beneath the Shockley-Queisser limit, despite its significant potential for solar cell applications. The present focus is on investigating potential multicomponent perovskite candidates, particularly on the application of machine learning to expedite band gap screening. To efficiently identify high-performance perovskites, we utilized a data set of 1346 hybrid organic-inorganic perovskites and employed 11 machine learning models, including decision trees, convolutional neural networks (CNNs), and graph neural networks (GNNs). Four descriptors were utilized for high-throughput screening: sine matrix, Ewald sum matrix, atom-centered symmetry functions (ACSF), and many-body tensor representation (MBTR). The results indicated that LightGBM and CatBoost somewhat surpassed XGBoost in decision tree models, but random forests lagged. Among the CNN models utilizing the same four descriptors, CustomCNN and VGG16 surpassed Xception, while EfficientNetV2B0 exhibited the least favorable performance. When the sine matrix and Ewald sum matrix served as adjacency matrices in GNN models, GCSConv exhibited a considerable improvement over GATConv and a slight advantage over GCNConv. Significantly, GCSConv outperformed other models when utilized with the Ewald sum matrix. The ideal combination of descriptors and algorithms identified was MBTR + CustomCNN, with an R2 of 0.94. Subsequently, three perovskites exhibiting appropriate Heyd-Scuseria-Ernzerhof (HSE06) band gaps were identified to define the defects. Among them, CH3C(NH2)2SnI3 exhibited superior performance in both vacancy and substitutional defects compared to C3H8NSnI3 and (CH3)2NH2SnI3. This high-throughput screening method with machine learning establishes a robust foundation for selecting solar materials with exceptional photoelectric properties.

Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies

In the post-Moore's law era, the progress of electronics relies on discovering superior semiconductor materials and optimizing device fabrication. Computational methods, augmented by emerging data-driven strategies, offer a promising alternative to the traditional trial-and-error approach. In this Perspective, we highlight data-driven computational frameworks for enhancing semiconductor discovery and device development by elaborating on their advances in exploring the materials design space, predicting semiconductor properties and optimizing device fabrication, with a concluding discussion on the challenges and opportunities in these areas.

Prediction of Organic-Inorganic Hybrid Perovskite Band Gap by Multiple Machine Learning Algorithms

As an indicator of the optical characteristics of perovskite materials, the band gap is a crucial parameter that impacts the functionality of a wide range of optoelectronic devices. Obtaining the band gap of a material via a labor-intensive, time-consuming, and inefficient high-throughput calculation based on first principles is possible. However, it does not yield the most accurate results. Machine learning techniques emerge as a viable and effective substitute for conventional approaches in band gap prediction. This paper collected 201 pieces of data through the literature and open-source databases. By separating the features related to bits A, B, and X, a dataset of 1208 pieces of data containing 30 feature descriptors was established. The dataset underwent preprocessing, and the Pearson correlation coefficient method was employed to eliminate non-essential features as a subset of features. The band gap was predicted using the GBR algorithm, the random forest algorithm, the LightGBM algorithm, and the XGBoost algorithm, in that order, to construct a prediction model for organic-inorganic hybrid perovskites. The outcomes demonstrate that the XGBoost algorithm yielded an MAE value of 0.0901, an MSE value of 0.0173, and an R2 value of 0.991310. These values suggest that, compared to the other two models, the XGBoost model exhibits the lowest prediction error, suggesting that the input features may better fit the prediction model. Finally, analysis of the XGBoost-based prediction model's prediction results using the SHAP model interpretation method reveals that the occupancy rate of the A-position ion has the greatest impact on the prediction of the band gap and has an A-negative correlation with the prediction results of the band gap. The findings provide valuable insights into the relationship between the prediction of band gaps and significant characteristics of organic-inorganic hybrid perovskites.

Degradation Kinetics of Organic-Inorganic Hybrid Materials from Micro-Raman Spectroscopy and Density-Functional Theory: The Case of β-ZnTe(en)0.5

Organic-inorganic hybrid materials often face a stability challenge. β-ZnTe(en)0.5 , which uniquely has over 15-year real-time degradation data, is taken as a prototype structure to demonstrate an accelerated thermal aging method for assessing the intrinsic and ambient-condition long-term stability of hybrid materials. Micro-Raman spectroscopy is used to investigate the thermal degradation of β-ZnTe(en)0.5 in a protected condition and in air by monitoring the temperature dependences of the intrinsic and degradation-product Raman modes. First, to understand the intrinsic degradation mechanism, the transition state of the degradation is identified, then using a density functional theory, the intrinsic energy barrier between the transition state and ground state is calculated to be 1.70 eV, in excellent agreement with the measured thermal degradation barrier of 1.62 eV in N2 environment. Second, for the ambient-condition degradation, a reduced thermal activation barrier of 0.92 eV is obtained due to oxidation, corresponding to a projected ambient half-life of 40 years at room temperature, in general agreement with the experimental observation of no apparent degradation over 15 years. Furthermore, the study reveals a mechanism, conformation distortion enhanced stability, which plays a pivotal role in forming the high kinetic barrier, contributing greatly to the impressive long-term stability of β-ZnTe(en)0.5 .

Emerging Two-Dimensional Organic Semiconductor-Incorporated Perovskites─A Fascinating Family of Hybrid Electronic Materials

Halide perovskites have attracted a great amount of attention owing to their unique materials chemistry, excellent electronic properties, and low-cost manufacturing. Two-dimensional (2D) halide perovskites, originating from three-dimensional (3D) perovskite structures, are structurally more diverse and therefore create functional possibilities beyond 3D perovskites. The much less restrictive size constraints on the organic component of these hybrid materials particularly provide an exciting platform for designing unprecedented materials and functionalities at the molecular level. In this Perspective, we discuss the concept and recent development of a sub-class of 2D perovskites, namely, organic semiconductor-incorporated perovskites (OSiPs). OSiPs combine the electronic functionality of organic semiconductors with the soft and dynamic halide perovskite lattice, offering opportunities for tailoring the energy landscape, lattice and carrier dynamics, and electron/ion transport properties for various fundamental studies, as well as device applications. Specifically, we summarize recent advances in the design, synthesis, and structural analysis of OSiPs with various organic conjugated moieties as well as the application of OSiPs in photovoltaics, light-emitting devices, and transistors. Lastly, challenges and further opportunities for OSiPs in molecular design, integration of novel functionality, film quality, and stability issues are addressed.

Understanding systematic growth mechanism of porous Zn1-xCdxSe/TiO2 nanorod heterojunction from ZnSe(en)0.5/TiO2 photoanodes for bias-free solar hydrogen evolution

Herein, a porous Zn_1-xCd_xSe structure was developed on TiO₂ nanorod (NR) array for photoelectrochemical (PEC) application. Firstly, TiO₂ NR and ZnO/TiO₂ NR photoanode were synthesized via a series of hydrothermal methods on FTO. Next, the solvothermal synthesis method was adopted to develop inorganic-organic hybrid ZnSe(en)_0.5 on ZnO /TiO₂ NR-based electrode using different concentrations of the selenium (Se). We found that the ZnO NR acts as a mother material for the formation of inorganic-organic hybrid ZnSe(en)_0.5, whereas TiO₂ NR acts as a building block. In order to further improve the PEC charge transfer performance, inorganic-organic hybrid ZnSe(en)_0.5/TiO₂ NR electrode was transferred into a porous Zn_1-xCd_xSe/TiO₂ NR photoanode using the Cd²⁺ ion-exchange method. The optimized porous Zn_1-xCd_xSe/TiO₂ NR -(2) photoanode converted from ZnSe(en)_0.5 -(2) electrode (optimized Se concentration) showed a higher photocurrent density of 6.6 mA·cm^-2 at applied potential 0 V vs. Ag/AgCl. The enhanced photocurrent density was owing to the effective light absorption, enhanced charge separation, delay the charge recombination, and porous structure of Zn_1-xCd_xSe. This work highlights the promising strategy for the synthesis of porous Zn_1-xCd_xSe/TiO₂ NR from inorganic-organic ZnSe(en)_0.5/TiO₂ NR for effective charge separation and prolonging the lifetime during the photoelectrochemical reaction.

Bulk Perovskite Crystal Properties Determined by Heterogeneous Nucleation and Growth

In metal halide perovskites, charge transport in the bulk of the films is influenced by trapping and release and nonradiative recombination at ionic and crystal defects. Thus, mitigating the formation of defects during the synthesis process of perovskites from precursors is required for better device performance. An in-depth understanding of the nucleation and growth mechanisms of perovskite layers is crucial for the successful solution processing of organic-inorganic perovskite thin films for optoelectronic applications. In particular, heterogeneous nucleation, which occurs at the interface, must be understood in detail, as it has an effect on the bulk properties of perovskites. This review presents a detailed discussion on the controlled nucleation and growth kinetics of interfacial perovskite crystal growth. Heterogeneous nucleation kinetics can be controlled by modifying the perovskite solution and the interfacial properties of perovskites adjacent to the underlaying layer and to the air interface. As factors influencing the nucleation kinetics, the effects of surface energy, interfacial engineering, polymer additives, solution concentration, antisolvents, and temperature are discussed. The importance of the nucleation and crystal growth of single-crystal, nanocrystal, and quasi-two-dimensional perovskites is also discussed with respect to the crystallographic orientation.