Machine Learning-Directed Predictive Models: Deciphering Complex Energy Transfer in Mn-Doped CsPb(Cl_1–yBr_y)₃ Perovskite Nanocrystals

Abstract

Lead halide perovskite nanocrystals with inclusion of a transition-metal dopant of Mn 2+ offer a substantial degree of freedom to modulate the optoelectronic and magnetic properties owing to the introduced dopant in the host lattices. However, complexity as a result of the excited interactions between the exciton and dopant, involving dynamics of exciton recombination, competing forward and backward energy transfer (and vice versa), and Mn recombination, makes it difficult to understand and predict the Mn sensitization. Here, we have created machine learningdirected models using different nonlinear algorithms with initial 86 samples to decipher the complex energy transfer by navigating the reaction design space of various concentrations of Mn along with different halide compositions (band gap) in Mn-doped CsPb(Cl 1−y Br y ) 3 nanocrystals. Knearest neighbor-based predictive models coupled with time-correlated single photon counting measurements allow for fully elucidating the complex and competing energy transfer kinetics occurring in two different Mn concentration regimes. Importantly, forward exciton-to-Mn energy transfer is more governed by the Mn concentration, while the backward Mn-to-exciton energy transfer is strongly dependent on the energy gap difference between the exciton and Mn energy state. This machine learning-guided approach and modeling can not only provide an efficient means for navigating the vast reaction design space but also provide significant insight into understanding and elucidating the complex physical phenomena throughout analyzing and predicting the dataset trend.

Dual-Emissive Mn-Doped Lead Halide Perovskite Nanocrystals as Background-Suppressed Latent Fingerprint Detection Probes

“…Figure D summarizes the calculated PL band ratio as a function of the Mn/Pb ratio, demonstrating that the PL band ratio gradually increased from 0.3 to 19 as the Mn/Pb ratio was increased to 8. It is important to note that controlling the dual band emission is directly associated with the exciton-to-dopant energy-transfer efficiency (inset to Figure D), which is governed by the dopant Mn concentration and the band gap. , Intermediate Mn/Pb ratios in the range of 3.0–4.0 exhibit comparable PL emission intensities from both emissive centers (exciton and Mn). In the case of Mn/Pb = 8, the increased energy-transfer efficiency from the exciton to Mn results in a significant loss of the excitonic emission (420–450 nm).…”

Machine-Learning-Driven Discovery of Mn4+-Doped Red-Emitting Fluorides with Short Excited-State Lifetime and High Efficiency for Mini Light-Emitting Diode Displays

“…RF is an ensemble learning method based on decision trees for classification, regression, and other tasks, which is known as one of the most accurate algorithms, since a large number of trees give a more robust model . Importantly, RF can be used to rank the importance of features (Figure S4), which is a very important tool for analyzing and revealing the “structure–property” relationship . Therefore, the RF was used to build the ML model capable of accurately predicting the ESLs of Mn 4+ in fluorides, namely, the RF model.…”

Exciton-to-Dopant Energy Transfer Dynamics in Mn2+ Doped CsPbBr3 Nanowires Synthesized by Diffusion Doping

“…Due to their unique optical, electrical, and opto-magnetic properties, Mn 2+ doped semiconductor nanocrystals (NCs) have attracted widespread attention, making them promising candidates for various optoelectronic applications. , Recent advances have successfully incorporated Mn 2+ into metal halide perovskite NCs, providing an attractive alternative to traditional semiconductor NCs. − Similar to Mn 2+ doped chalcogenide NCs, Mn 2+ ion doped perovskite NCs with a wider energy gap can be excited to achieve characteristic dopant emission at 2.1 eV due to the spin-flip transition from the 4 T 1 excited state to the 6 A 1 ground state by energy transfer from host excitons to Mn 2+ . − The Mn photoluminescence (PL) mechanism was demonstrated by Brovelli et al, who elucidated a two-step process involving exciton localization in a shallow metastable state and mediating the thermally assisted sensitization of the Mn 2+ emission in Mn 2+ doped CsPbCl 3 NCs . Furthermore, some studies have indicated efficient back energy transfer from Mn 2+ to excitons, particularly in perovskite NCs with lower energy gaps. − Nevertheless, the comprehension of energy transfer dynamics in Mn 2+ -doped perovskite NCs with various quantum confinement degrees and dimensions, such as nanowires (NWs), remains unexplored. Understanding the excited-state coupling between the exciton and dopants as affected by wave function overlapping is crucial for optimizing energy transfer and Mn 2+ luminescence efficiency.…”

“…This problem can be solved by reducing the NC size to increase the bandgap based on the quantum confinement effect. On the other hand, the enhanced coupling between the wave function of NCs and Mn 2+ ions should lead to enhanced energy transfer efficiency. , Second, it is challenging to introduce Mn 2+ dopants through direct colloidal synthesis of CsPbBr 3 NCs using MnBr 2 precursors, which are dissolved in the reaction mixture as Mn-oleate complexes containing Mn–O bonds. , This difficulty arises from the higher bond dissociation energy of the Mn–O bond (402 kJ/mol) compared to that of the Pb–Br bond (248.5 kJ/mol). , In other words, a substantial driving force is necessary to incorporate Mn 2+ into the Mn:CsPbBr 3 product from the Mn-oleate precursor. , Consequently, the synthesis of colloidal Mn:CsPbBr 3 NCs with bright Mn 2+ luminescence has proven to be a formidable task.…”