Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields

Abstract

The development of functional organic fluorescent materials calls for fast and accurate predictions of photophysical parameters for processes such as high-throughput virtual screening, while the task is challenged by the limitations of quantum mechanical calculations. We establish a database covering >4,300 solvated organic fluorescent dyes and develop new machine learning (ML) approach aimed at efficient and accurate predictions of emission wavelength and photoluminescence quantum yield (PLQY). Our feature engineering has given rise to Functionalized Structure Descriptor (FSD) and Comprehensive General Solvent Descriptor (CGSD), whereby a highly black-box computational framework is realized with consistently good accuracy across different dye families, ability of describing substitution effects and solvent effects, efficiency for large-scale predictions and workability with on-the-fly learning. Evaluations with unseen molecules suggests a remarkable MAE of 0.13 for PLQY and 0.080 eV for emission energy, the latter comparable to time-dependent density functional theory (TD-DFT) calculations. An online prediction platform was constructed based on the ensemble model to make prediction in various solvents (https://www.chemfluor.top/). Our statistical learning methodology will complement quantum mechanical calculations as an efficient alternative approach for the prediction of these parameters. File list (2) download file view on ChemRxiv Manuscript_20201029.pdf (1.05 MiB) download file view on ChemRxiv SupportingInformation_update.pdf (2.71 MiB)

Multi-fidelity prediction of molecular optical peaks with deep learning

“…a single dye family) because of the limited availability of large UV/Vis datasets. This data sparsity has been addressed recently with the publication of several experimental datasets 1,[11][12][13][14][15][16][17][18] , described in Table 1. There are also several large computed datasets of excitation energies available (Table 2).…”

“…"Full" refers to the full absorption/emission spectrum as xy-coordinate pairs, λ max is the peak wavelength, ε max is the peak molar attenuation coefficient (also called the molar extinction coefficient or molar absorptivity), σ is the peak FWHM (bandwidth), Φ is the quantum yield, and τ is the fluorescence lifetime. A subset of the data in the ChemFluor16 set was extracted from the Fluorophores 12 set. The number of entries for the UV/Vis+ dataset includes the count of the dye entries only, and the entries for NIST do not include ions…”

Controlling Molecular Aggregation-Induced Emission by Controlled Polymerization

“…In parallel with the understanding of their structure–property relationships, the question of how to further fabricate efficient stimuli-responsive systems with controllable and significant signal outputs by interfering with one of the four key parameters could provide a promising future research direction, in regard to their multi-tunable fluorescence properties (e.g., emission color). Meanwhile, during this process, machine learning could be a powerful tool to predict the photophysical properties of the designed AIE systems and thus reduce the experimental workload [ 115 , 116 ]. The combination of controlled polymerization, AIE molecular synthesis, theoretical calculation and machine learning could potentially lead to the development of novel fluorescent materials with real-world applications in the near future.…”

A study of boosting molecular descriptors with quantum-derived features in prediction of maximum emission wavelengths of chromophores.

“…There is possibility to polish the method to give better predictions. Some other applications of machine learning in predictions of organic compounds emission wavelengths were published [24,25].…”

“…This phenomenon is well known in cheminformatics as class imbalanced data [26,27,28]. Cheng-Wei et al [25] calculated a curated number of molecular descriptors of solvents which appears to be the correct way to preserve database diversity. Since its vulnerability to database correspondence to compound being assessed, the method should be provided with proper database.…”