Design of circularly polarized phosphorescence materials guided by transfer learning

LLMs for precursor selection and CPP performance regulation

In the preparation of CPP materials with high g_lum values, high quantum efficiency and long lifetime, phosphorescent molecule selection plays a crucial role in governing the preparation pathway. To extract the complex and diverse information embedded in chemical literature, LLM was used to choose suitable phosphorescent molecules (Fig. 2a). We collected over 500 documents on the design and discovery of phosphorescent materials, downloaded them as PDF files and processed them into vector databases using LangChain. Utilizing RAG technology, we summarized the textual information and compiled these summaries into a database. This approach processes scientific articles in their entirety. It creates vector embeddings of textual content (paragraphs), then retrieves relevant information based on the semantic similarity of the text, and augments the LLM’s generation with this contextually relevant information. Furthermore, we enabled Long Context mode in LLMs to maintain a detailed record of extended textual content, thus facilitating the processing and generation of complex data sets. Our system processes chemical information as it naturally appears within scientific literature. The LLM processes this information contextually, including any SMILES strings, chemical formulas, or descriptive text about molecular properties that might be present in the retrieved passages. This approach allows for a more natural and comprehensive handling of chemical information within the framework of scientific knowledge (Supplementary Fig. 1). To focus our study, we applied constraints such as room-temperature, long lifetime, and high quantum efficiency, leading to a list of phosphorescent molecules generated by the LLM (Supplementary Table 1). From this list, we manually selected three molecules: 1-pyrenylboronic acid (PA), 7H-dibenzo[c,g]-carbazole (DBCz), and 11,12-dihydroindolo[2,3-a]carbazole (ICz). These molecules all exhibited similar blue fluorescence emissions, but different phosphorescence emissions, including blue for ICz, green for DBCz and red for PA, when they were compounded with polyvinyl alcohol (PVA), respectively (Fig. 2a and Supplementary Figs. 2–4). All chiroptical films were prepared by overlaying one transparent oriented PVA layer (as a phase retarder) onto another highly oriented dyed PVA layer (as a polarizer with selective absorption) in a twisted fashion to fabricate a twisted stacking structure³⁰. Large optical activities can be observed when the twist angle is ±45° (Supplementary Fig. 5), in accordance with the prediction based on Jones Matrix mechanism⁴⁶. There are several variable parameters, including the thickness and stretching degree of the transparent oriented PVA layer, greyscale and dye molecules selection of highly oriented dyed films as well as the twist angle, which greatly affect their optical activities (Supplementary Figs. 6–9). Then, CPP materials can be obtained by integrating isotropic phosphorescent molecules/PVA hybrid films with above twisted stacking structure films (Fig. 2b). Their CPL and CPP performance could be defined by asymmetry factor g_lum (g_lum = 2(I_L–I_R)/(I_L + I_R)), where I_L and I_R were the emissions of left-handed and right-handed circularly polarized light. The generation of CPL and CPP could be understood based on a Muller matrix for a combination of polarizer and phase retarder²⁵. The linearly polarized fluorescence (phosphorescence) would be produced after passing through the highly oriented dyed PVA layer (as the polarizer), and then CPP would be produced after passing through the transparent oriented PVA layer (as a phase retarder) in a twisted fashion (Fig. 2b). In total more than 10⁸ possible combinations are estimated to achieve different CPP performance. Flexibly manipulating the CPP performance in a simple way always remains challenging¹⁵. By altering the above variable structure/processing parameters and their combinations, a continuous modulation of CPP performance including the sign, magnitude, peak position as well as FWHM could be achieved (Fig. 2c–e and Supplementary Figs. 10–12).

a Flowchart of molecular recommendation by LLMs. b Schematic illustration of the generation of CPP through the twisted stacking structure. CPP performance of the samples with (c) different g_lum values at around 650 nm by adjusting the thickness and strain of transparent oriented PVA layer. d Different peak position but similar g_lum values by varying the strain of transparent oriented PVA layer and the dye molecules. e Different FWHM by varying the combinations of the thickness and strain of transparent oriented PVA layer as well as the dye molecules.

Transfer learning model for forward prediction

Due to the richness of design space, precise forward prediction of the CPP performance of the hybrid films based on traditional trial-and-error approaches seemed unlikely, since these variable parameters often correlated with each other. In our previous work, we established a quantitative structure-spectrum-function relationship between the structural parameters, spectral features and circular dichroism (CD) properties of the twisted stacking structure, and realized precise forward prediction based on machine learning techniques²⁹. Herein, the CD dataset of the twisted-stacking structures in our previous work was recruited to solve this problem. Predicting the CD spectra was adapted as a pre-training task for assisting the prediction of g_lum for CPP materials. To further enhance the accuracy of the forward prediction model, 135 combinations of parameters were employed to construct CPP hybrid films, and their CPL as well as CPP performance was measured. The fluorescence spectrometer was used to measure the emission spectra of the composite film in the presence of left-handed and right-handed circular polarizers. Then, according to the formula CPL = I_L − I_R, the circular polarized luminescent properties of the composite film were obtained. Furthermore, based on the formula g_lum = 2(I_L–I_R)/(I_L + I_R), the spectrum of g_lum varying with the wavelength was calculated, which was used as the data source for model training (Supplementary Figs. 13–15). This dataset was used for both training and validation of the forward prediction model. To efficiently simulate the g_lum spectra properties of the CPP materials, a forward prediction model based on transfer learning has been developed. Given the spectroscopic properties of g_lum, the forward prediction neural network was modeled as a multivariate regression problem. As shown in Fig. 3a, the model input consisted of two components: one was the spectral embedding descriptor, which included various structural and process parameters such as thickness, the stretching degree, grayscale, twist angle, and dye absorption. The composition of the spectral embedding descriptor vector is detailed in Supplementary Table 2. The other input is the emission spectrum of the phosphorescent molecule, exhibiting a wavelength range spanning from 400 to 700 nm and a data interval set at 0.2 nm. To illustrate the validity of the selected structural and process parameters, we analyzed the correlation among the four features using a correlation coefficient heatmap. The four elements in Fig. 3b represent the types of polarization layer dyes and phosphorescent molecules, the thickness of the phase delay layer, and the stretching degree of the phase delay layer, respectively. While the non-primary diagonal elements in the figure are close to 0, indicating that there is no linear correlation between the variables A, B, C, and D. The results indicated that these variables were completely independent of each other, which further validated the balance and effectiveness of the dataset (Fig. 3b). During the pre-training phase, an encoder-decoder architectural framework was utilized to map the structural and process parameters of the film into an embedding space, subsequent to which the embedded information was decoded for the generation of CD spectra. The latent representations in the embedding space were extracted, implicitly containing information on the absorption properties of chiral films, facilitating knowledge transfer and supporting the subsequent integrated modeling of g_lum spectra. In the fine-tuning phase, the model used the pre-trained encoder to generate latent features. These latent features were concatenated with the phosphorescent molecular emission spectra and fed into a Multi-Layer Perceptron (MLP), which output a reference vector x of the same dimension as the g_lum spectrum. Considering the different modulation mechanisms of process/structural parameters on the g_lum spectrum, a parameter encoder was also designed to encode the process/structural parameters into offset factor β and weight factor α. The implementation details of the parameter encoder were introduced in the Method-Model Architecture section. Using these two factors, the reference vector x was further adjusted to generate the final prediction result λ, as expressed in the following equation:

We established a model evaluation scheme with 10-fold cross-validation and employed five evaluation metrics to assess the model performance, including mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R²) and Pearson correlation coefficient (Pearson). For different models, higher MSE, MAE, and RMSE values indicate worse model performance, while higher R² and Pearson values reflect better performance. We comparatively analyzed our model with several other machine learning regression models including Random Forest (RF), Support Vector Regression (SVR), and XGBoost algorithms. Our model demonstrated the best predictive performance, with the lowest MAE of 0.14 and the highest R² of 0.89. Furthermore, our model exhibited a relatively lower standard deviation in the predicted results, indicating its superior consistency and stability (Fig. 3c and Supplementary Table 3). Figure 3d visualizes the g_lum predictions of a test sample against experimental fittings, showcasing the model’s prediction capability. The predicted curve closely aligns with the experimental curve, achieving an excellent R² of 0.993. The points in the scatter plot predominantly follow the line of y = x, suggesting a strong consistency between the predicted and experimental values. Additionally, to evaluate the effectiveness of the parameter encoder and transfer learning, ablation experiments were designed to compare performance differences with the absence of various components. Compared to models missing the offset factor, weight factor, or both, the original complete parameter encoding resulted in better predictive performance, indicating that both encoding factors contribute to more effective learning of the structure-spectrum-function relationship (Fig. 3e and Supplementary Table 4). To demonstrate the necessity of adopting transfer learning, we compared our model with the model without transfer learning. The result showed our approach significantly improved prediction accuracy. The MAE decreased from 0.24 to 0.14 (Fig. 3f and Supplementary Table 5), demonstrating that the material latent representations extracted via transfer learning effectively capture the absorption characteristics of the film materials. In order to further illustrate the predicted effect of our model and its practical application value, we also explored the model’s zero-shot regression capability. We prepared CPP materials in which methyl orange was used for the dye layer in the chiral film and our model had not been trained on experimental data for this dye. The robustness and generalization potential of the method was demonstrated by comparing the curves given by the model with the curves obtained from the experiments (Fig. 3g). As a polarized dye present in a pre-trained database, it is possible to predict the g_lum spectrum of the CPP material made from this dye, even if the model has not been trained on related data.

a The framework of transfer learning. b Correlation heat map of the features. c Mean square error, mean absolute error, root mean square error, coefficient of determination and Pearson correlation coefficient histogram of the models. d Fitting curves of experimental and predicted results. e The curve of MSE, MAE, RMSE, R² and Pearson correlation coefficient for models with different encoding factors. f Histogram of mean square error, mean absolute error, root mean square error, coefficient of determination and Pearson correlation coefficient for models with and without pretraining. g Zero-shot generalization of forward prediction model.

Inverse design of CPP materials

The established structure-spectrum-function relationship of CPP materials based on transfer learning can be further exploited in an inverse model for personalized customization of CPP materials with target functionality. Utilizing a forward prediction model, a virtual database was established, containing various predefined structural parameter combinations and their corresponding g_lum spectra, generated by the forward model. Therefore, the predefined structural parameter combinations expanded the existing dataset of 135 samples, which includes all possible combinations of 41 different stretching degrees (ranging from 80% to 120%) and other parameters within original ranges, totaling 1,107 data points. Researchers can formulate structural expert rules based on specific experimental scenarios and retrieve data from the virtual database according to these rules. After comparison and selection, the system will generate all possible structural parameter combinations that meet the required criteria, and the inverse design system was established, as outlined in Fig. 4a. The target CPP performance is user-defined and can take any form expressible from the CPP spectral features, e.g., a certain g_lum value (1.5) at a certain wavelength (450 nm, 550 nm, and 650 nm, respectively). Upon inputting the target g_lum value and corresponding wavelength into the inverse design system, a set of structure/process parameters combinations and phosphorescent molecule selections was generated, as shown in Fig. 4b. There are typically many available answers for a given target, since the hybrid films with different phosphorescent molecules and various structure/process parameters combinations would exhibit the same g_lum value at a certain wavelength. To evaluate the accuracy of the inverse design system, one of the possible parameters combinations was chosen to experimentally construct the hybrid film. As shown in Fig. 4c and  4d, the experimentally measured g_lum spectra matched well with the predicted spectra and most of the data points in the scatter plot align closely with the y = x line, with an R² value of 0.997, indicating its excellent accuracy. Figure 4e contains a histogram for the inversely designed and as-prepared films that can generate g_lum value of 1.5 at 450 nm, 550 nm, and 650 nm, respectively. The output of the inverse design system always consistently satisfies the specified design requirements (Supplementary Fig. 16), with errors maintained below 10%. Moreover, when a series of g_lum values (0.5, 0.9, 1.3, 1.5, and 1.7) at a certain wavelength (e.g. around 550 nm) were needed, the inverse design system can successfully report a set of possible selections of phosphorescent molecules and structure/process parameters combinations. Compared with the predicted spectra, the experimentally measured g_lum spectra exhibited excellent accuracy (Fig. 4f and Supplementary Fig. 17), confirming the success of our inverse design system for CPP films with target g_lum value at a certain wavelength.

a Schematic diagram for the inverse design. b Parameters generated by inverse design. c Fitting curves and d scatter plots of experimental and predicted results. Actual values and expected values histograms for the samples of experimental realization using inverse design for the targets: e g_lum = 1.5 at 450 nm, 550 nm, 650 nm; and f g_lum = 0.5, 0.9, 1.3, 1.5, 1.7 at around 550 nm, respectively.

Inverse design of multicolor display

The successful achievement of the inverse design system in precise prediction of single g_lum values at a single wavelength for CPP materials inspired us to extend our method to multiple g_lum values at multiple wavelengths, to meet the demand of multicolor display. As illustrated in Fig. 5a, the resultant CPP color depended not only on the selection of phosphorescent molecules but also on the constitution of the hybrid films. Taking DBCz as an example, in the absence of the chiral layer, the DBCz/PVA hybrid films exhibited green broad bandwidth phosphorescent emission, as depicted in Fig. 5b and  5c. Overlaying a transparent oriented PVA layer (the stretching degree of 90% as a phase retarder) onto another highly oriented Congo Red dyed PVA layer (as a polarizer) was utilized to fabricate a twisted stacking chiral layer. It should be noted here that the formed chiral layer exhibited giant optical activity and frequency selective transmission, which can be exploited as a circular polarization-based filter (CPF) for multiplex color switching. As expected, two different phosphorescent spectra could be obtained through L- or R-CPF, respectively (Fig. 5b). Thus, a green-cyan color switching in phosphorescent emission could be observed when viewed through L-CPF. Similarly, green-reddish orange color switching in phosphorescent emission could be observed when viewed through R-CPF (Fig. 5c, chiral layer 1). As mentioned above, the effective phosphorescent color switching greatly depended on their giant optical activity and frequency selective absorption properties of the chiral layer. Interestingly, by only varying the stretching degree of the transparent PVA layer (e.g. 120%), the chiral layer exhibited corresponding red shift in CD characterizations (Supplementary Fig. 18). Therefore, different color switching in phosphorescent emission could be observed when viewed through L- or R-CPF, respectively (Fig. 5c chiral layer 2, and Supplementary Fig. 19). Notably, only increasing the stretching degree of the PVA layer induces an overall red shift in the g_lum spectrum (Fig. 5d and Supplementary Fig. 20), greatly enhancing its effective color switching range in phosphorescent emission. Each of the three different constitutions of the hybrid films (including ICz, DBCz and PA) could work as a phosphorescent emission switch among three prime colors. Together, these three systems constitute a circular polarization-based full-color phosphorescent display. Any color within the enclosed area in the CIE1931 chromaticity diagram could be achieved and further switched based on above inverse design model for selecting suitable phosphorescent molecules and structure/process parameters combinations. This color space covers ~145% of a typical sRGB color gamut found in commercial color displays (Fig. 5e). For comparison, the color gamut of our previous system without the help of inverse design only reached 67% of the sRGB. Furthermore, our method effectively narrows the full-width at half maximum (FWHM) of the emission spectra of the phosphorescent materials, which can be utilized to enhance the emission color purity. For instance, for DBCz/PVA, by selecting a chiral layer with Congo Red dye, thickness of 80 μm, and stretching degree of 110%, the color purity was increased to about 95%, with the corresponding CIE coordinates being (0.480, 0.503) (Supplementary Fig. 21).

a Schematic diagram for the CPP materials with controllable color switching performance in phosphorescent emission. b The phosphorescence (Ph), CD and circularly polarized phosphorescent (CPP) spectra of the DBCz/PVA hybrid chiral films. c Different color switching performance could be achieved by only varying the stretching degree of PVA layers (90% and 120%, respectively). d The influence of stretching on the g_lum value of the hybrid films. The data is sourced from a virtual database, which was generated under the following conditions: the phosphorescent molecule is DBCz, the dye of the polarization layer is Direct Blue 71, the thickness of the phase retardation layer is 48 μm, and the stretching degree of the phase retardation layer is varied from 80% to 120%. The interval of the stretching degree between each group of data is 1. e Color gamut of the CPP materials in CIE1931 chromaticity diagram. A typical sRGB color gamut is shown for comparison.

Inverse design of 4D encrypting information

It would be highly desirable to design and controllably synthesize CPP materials with programmable CPL and CPP performance, e.g. with the same or opposite handedness, which could serve as unique photonic components in chiral optoelectronics and nanophotonics. However, the customized manufacturing of CPP materials with tailorable CPL and CPP properties would be very difficult based on traditional techniques such as the utilization of chiral dopants or chiral liquid crystals. But the success of inverse design for the chiral layer with multiple g_abs values at multiple wavelengths allows us to easily customize programmable CPL and CPP performance from the same phosphorescent layer. As shown in Fig. 6a, the target function was user defined (CPL and CPP performance with the same or opposite handedness) and was input into the computational brain, which could be processed with the inverse design model and a set of structure/processing parameters that approaches the target function were reported. For example, we defined left-handed blue CPL but right-handed green CPP as the target function, which was input into the inverse design model, and the suitable structure/processing parameters were given (Supplementary Fig. 22). As expected, only blue CPL “中” image could be viewed through L-CPF, while green CPP “中” image could be viewed through R-CPF, indicating that the produced sample exhibited the customized CPL and CPP performance with opposite handedness (Fig. 6b). Interestingly, we defined right-handed blue CPL and green CPP as the target function, and the sample could be successfully fabricated with right-handed blue CPL and green CPP performance based on the inverse design model. Similarly, the target function with same handed blue CPL and CPP or opposite handed blue CPL and red CPP performance could be realized successfully (Supplementary Figs. 23 and 24), experimentally demonstrating the excellent accuracy of our inverse design model. All above results indicated the successful customization of programmable CPL and CPP performance at multiple predesigned wavelengths.

a Schematic diagram for the inverse design of CPP films with programmable CPL and CPP performance and b their fluorescence and phosphorescence images viewed with naked eyes, through L- or R-CPF, respectively. c Encoding terminology of 4D information encryption and decryption.

Further, we attempted to combine their programmable CPL and CPP characteristics to achieve 4D information encryption. As shown in Fig. 6c, the color of the phosphorescence emission is used as the first dimensional code (1^stD code). The blue phosphorescent emission represents ‘0’, while others represent ‘1’. The handedness of the CPP could be used as the second dimensional code. The left-handed CPP represents ‘0’, while right-handed CPP represents ‘1’. As mentioned above, different color switching in phosphorescent emission could be observed through L- or R-CPF, respectively. By varying only the stretching degree of the PVA layer, different color switching range in phosphorescent emission could be achieved even from the same phosphorescent layer. Similarly, programmable CPL and CPP properties with same or opposite handedness could also be achieved from the same phosphorescent layer. Consequently, the effective color switching in phosphorescence emission and programmable CPL and CPP properties could be used as two other dimensions of encryption (Fig. 6c). Such a 4D bar code is much more complex than the other traditional system using only color, lifetime and chirality as the dimensions of encryption. Taking advantage of the above multi-mode characteristics of our 4D information encryption system, the information capacity and security would be greatly enhanced, holding significant potential applications for safeguarding valuable and authentic information.