TL;DR
A unified multiscale system that entails the implementation of density functional theory (DFT), machine learning (ML), and device-level simulation to hasten the search and development of high-performance perovskite solar cell materials is presented.
Predicting electrical conductivity in perovskite and double-perovskite materials remains challenging as this property depends on various electronic, chemical, and structural factors. In this work, we evaluate classical machine-learning models trained on different descriptor sets including DFT band gap, non-orbital compositional descriptors, orbital-related descriptors, SOAP structural fingerprints, and a reduced mixed descriptor set to predict DFT-derived transport conductivity. The band gap provides a strong baseline but is insufficient to fully predict the target. The best overall performance is obtained using non-orbital compositional descriptors with Random Forest regression, while orbital-related descriptors achieve nearly comparable accuracy, confirming the importance of valence-electron characteristics. A compact mixed descriptor set preserves nearly the full predictive power of the larger descriptor spaces, showing that accurate prediction can be achieved using a small number of physically motivated variables. In contrast, SISSO showed much lower accuracy, suggesting that sparse symbolic expressions are insufficient to capture the nonlinear relationships underlying our target values. These results demonstrate that physically informed classical machine-learning models can provide an effective surrogate framework for reproducing DFT/BoltzTraP-derived conductivity trends in perovskite and double-perovskite materials.
Machine learning has become an effective tool for accelerating materials discovery by predicting material properties with far lower computational cost than exhaustive first-principles calculations. However, its application to dopant engineering in two-dimensional (2D) oxide semiconductors remains relatively unexplored. In this work, we combine supervised machine learning with density functional theory-derived data from the Materials Project database to predict the electronic, thermodynamic, transport, and optical properties of undoped and doped 2D tin monoxide (SnO). Several supervised regression algorithms are evaluated for predicting the band gap, formation energy, carrier mobility, electrical conductivity, and optical absorption coefficient. Among the models considered, gradient boosting regression provides the highest predictive accuracy and consistently reproduces the complex structure–property relationships of both undoped and doped systems. Feature-importance analysis reveals that electronic band descriptors, particularly band width, followed by band asymmetry and electronic anisotropy, are the primary factors governing band-gap prediction, while structural descriptors provide complementary contributions. The predictions indicate that monolayer SnO has a wider band gap, slightly higher formation energy, and higher carrier mobility than bulk SnO due to quantum confinement. Mg and Zn substitution allow systematic tuning of the material properties over a moderate concentration range. The band gap changes only slightly with doping, while Mg incorporation is thermodynamically more favorable than Zn, whose stability decreases with increasing concentration. Dopant-induced impurity scattering reduces carrier mobility and electrical conductivity, whereas optical absorption is enhanced in both Mg- and Zn-doped systems. These results demonstrate that machine learning can efficiently identify promising dopant configurations while providing reliable predictions of dopant-dependent material properties. The proposed framework offers a practical approach for the computational design of doped 2D oxide semiconductors for future optoelectronic and sensing applications.
An explainable machine-learning framework was developed for dielectric constant prediction using 52,168 crystalline materials extracted from the Joint Automated Repository for Various Integrated Simulations (JARVIS-DFT) database, demonstrating the complementary roles of electronic structure and elemental chemistry.
The optoelectronic properties of the lead-free perovskite CsSn0.5Ge0.5I3 are investigated by first-principles calculations and numerical simulations using SCAPS-1D. The energy-level alignment between transport layers and the perovskite layer is evaluated, resulting in the establishment of the PCBM/CsSn0.5Ge0.5I3/PEDOT:PSS structure. Key parameters, including bulk defect density, layer thickness, and electrode materials, are optimised, and the effects of resistance, illumination intensity, thermal stability, and carrier generation-recombination rates on device performance are analysed. The optimal device structure FTO/PCBM/CsSn0.5Ge0.5I3/PEDOT:PSS/C achieves a power conversion efficiency (PCE) of 24.50% and a fill factor (FF) of 80.01%. Machine learning (ML) algorithms are applied to predict photovoltaic parameters, with Random Forest (RF) exhibiting the highest accuracy. SHAP analysis identifies absorber layer thickness as the dominant factor influencing efficiency, providing guidance for experimental optimisation. This integrated approach offers a practical pathway for designing high-performance, stable, and environmentally sustainable perovskite solar cells (PSCs).