Perspectives on development of optoelectronic materials in artificial intelligence age

Optoelectronic devices, such as light-emitting diodes, have been demonstrated as one of the most demanded forthcoming display and lighting technologies because of their low cost, low power consumption, high brightness, and high contrast. The improvement of device performance relies on advances in precisely designing novelty functional materials, including light-emitting materials, hosts, hole/electron transport materials, and yet which is a time-consuming, laborious and resource-intensive task. Recently, machine learning (ML) has shown great prospects to accelerate material discovery and property enhancement. This review will summarize the workflow of ML in optoelectronic materials discovery, including data collection, feature engineering, model selection, model evaluation and model application. We highlight multiple recent applications of machine-learned potentials in various optoelectronic functional materials, ranging from semiconductor quantum dots (QDs) or perovskite QDs, organic molecules to carbon-based nanomaterials. We furthermore discuss the current challenges to fully realize the potential of ML-assisted materials design for optoelectronics applications. It is anticipated that this review will provide critical insights to inspire new exciting discoveries on ML-guided of high-performance optoelectronic devices with a combined effort from different disciplines.

Accelerating the Discovery of Hybrid Perovskites with Targeted Band Gaps via Interpretable Machine Learning

The band gap of hybrid organic-inorganic perovskites (HOIPs) is a key factor affecting the light absorption characteristics and thus the performance of perovskite solar cells (PSCs). However, band gap engineering, using experimental trial and error and high-throughput density functional theory calculations, is blind and costly. Therefore, it is critical to statistically identify the multiple factors influencing band gaps and to rationally design perovskites with targeted band gaps. This study combined feature engineering, the gradient-boosted regression tree (GBRT) algorithm, and the genetic algorithm-based symbolic regression (GASR) algorithm to develop an interpretable machine learning (ML) strategy for predicting the band gap of HOIPs accurately and quantitatively interpreting the factors affecting the band gap. Seven best physical features were selected to construct a GBRT model with a root-mean-square error of less than 0.060 eV, and the most important feature is the electronegativity difference between the B-site and the X-site (χB-X). Further, a mathematical formula (Eg = χB-X2 + 0.881χB-X) was constructed with GASR for a quantitative interpretation of the band gap influence patterns. According to the ML model, the HOIP MA0.23FA0.02Cs0.75Pb0.59Sn0.41Br0.24I2.76 was obtained, with a suitable band gap of 1.39 eV. Our proposed interpretable ML strategy provides an effective approach for developing HOIP structures with targeted band gaps, which can also be applied to other material fields.

Interface Engineering at Sc2C MXene and Germanium Iodine Perovskite Interface: First-Principles Insights

In recent years, Ge-based halide perovskite has gained increasing attention due to its potential in the development of lead-free perovskite solar cells. Here, through first-principles calculations, we explored the possibilities to enhance the optoelectronic properties of Ge-based perovskites via interfacial engineering between germanium iodine perovskite and 2D scandium-carbide MXene with various termination groups including F, O, and OH. We first evaluated the relative stability of the material interfaces and found that MAI-terminated interfaces are energetically more favorable than the GeI₂-terminated interfaces. The MAI/F interface exhibits a type-II band alignment that can promote the photogenerated electron-hole separation. Moreover, the work function of the heterostructures can be tuned from 2.60 to 4.45 eV via using various termination groups. Additionally, 2D Sc₂C MXene can also significantly enhance the light absorption. These results indicate that the 2D MXene serves as one promising candidate for optimizing the properties of perovskite solar cells via interface engineering.

High-throughput screening of perovskite inspired bismuth halide materials: toward lead-free photovoltaic cells and light-emitting diodes

Toxicity is the main bottleneck for the commercialization of Pb halide perovskites. Bi has been considered a promising metal cation to replace Pb because of its comparable electronic structures with Pb and better stability. Although experimental and theoretical studies have proposed various Bi-based halides, the present achievements in photovoltaic cells and other photoelectronic device fields do not compete with Pb analogs. Thermodynamic stability, bandgap control, and enhancement of carrier transport are fundamental challenges in the context of intrinsic material properties for developing highly efficient Bi-based devices. This study evaluates the potential of Bi-based halide compounds with good stability and electronic properties through high-throughput density functional theory calculations. Lattice structures and compositions are selected based on previous reports and an open material database. Then, we expanded our dataset to cover all possible compositional variations of A- and X-sites and alloying to B-sites. We examined over six-hundred candidates and found ten new candidates that have not been reported previously. Rb₃SbBiI₉exhibits the best-expected efficiency for high-efficiency solar cells among selected compounds, and other compounds can be used as visible-light-generation sources. Analysis of the screening procedure revealed that vacancy-ordered (A₃B₂X₉)-type Bi-halides exhibit significantly favorable characteristics when compared with those of double perovskites and rudorffite-like structures for Bi-based photoelectronic devices.

Inorganic Crystal Structure Prototype Database Based on Unsupervised Learning of Local Atomic Environments

Recognition of structure prototypes from tremendous known inorganic crystal structures has been an important subject beneficial for materials science research and new materials design. The existing databases of inorganic crystal structure prototypes were mostly constructed by classifying materials in terms of the crystallographic space group information. Herein, we employed a distinct strategy to construct the inorganic crystal structure prototype database, relying on the classification of materials in terms of local atomic environments (LAEs) accompanied by unsupervised machine learning method. Specifically, we adopted a hierarchical clustering approach onto all experimentally known inorganic crystal structure data to identify structure prototypes. The criterion for hierarchical clustering is the LAE represented by the state-of-the-art structure fingerprints of the improved bond-orientational order parameters and the smooth overlap of atomic positions. This allows us to build up a LAE-based Inorganic Crystal Structure Prototype Database (LAE-ICSPD) containing 15,613 structure prototypes with defined stoichiometries. In addition, we have developed a Structure Prototype Generator Infrastructure (SPGI) package, which is a useful toolkit for structure prototype generation. Our developed SPGI toolkit and LAE-ICSPD are beneficial for investigating inorganic materials in a global way as well as accelerating the materials discovery process in the data-driven mode.

An Ensemble Learning Platform for the Large-Scale Exploration of New Double Perovskites

Lead-free double perovskites are regarded as stable and green optoelectronic alternatives to single perovskites, but may exhibit indirect band gaps and high effective masses, thus limiting their maximum photovoltaic efficiency. Considering that the trial-and-error experimental and computational approaches cannot quickly identify ideal candidates, we propose an ensemble learning workflow to screen all suitable double perovskites from the periodic table, with a high predictive accuracy of 92% and a computed speed that is ∼10⁸ faster than ab initio calculations. From ∼23 314 unexplored double perovskites, we successfully identify six candidates that exhibit suitable band gaps (1.0-2.0 eV), where two have direct band gaps and low effective masses. They all show good thermal stabilities that are hopefully able to be synthesized. The proposed ML workflow immensely shortens the screening cycle for double perovskites, which will greatly promote the development and application of photovoltaic devices.