Machine Learning-Based Screening for Potential Singlet Fission Chromophores: The Challenge of Imbalanced Data Sets

Abstract

Excitation with one photon of a singlet fission (SF) material generates two triplet excitons, thus doubling the solar cell efficiency. Therefore, the SF molecules are regarded as new generation organic photovoltaics, but it is hard to identify them. Recently, it was demonstrated that molecules of low-to-intermediate diradical character (DRC) are potential SF chromophores. This prompts a low-cost strategy for finding new SF candidates by computational high-throughput workflows. We propose a machine learning aided screening for SF entrants based on their DRC. Our data set comprises 469 784 compounds extracted from the PubChem database, structurally rich but inherently imbalanced regarding DRC values. We developed well performing classification models that can retrieve potential SF chromophores. The latter (∼4%) were analyzed by K-means clustering to reveal qualitative structure–property relationships and to extract strategies for molecular design. The developed screening procedure and data set can be easily adapted for applications of diradicaloids in photonics and spintronics.

Computational Discovery of Intermolecular Singlet Fission Materials Using Many-Body Perturbation Theory

“…Such models may be physical/chemical models, such as the multiradical character, or machine learned (ML) models. The vast majority of chemical space exploration efforts have focused on isolated molecules. ,,− ,− Here, we discuss two examples focused on crystalline materials.…”

“…The vast majority of chemical space exploration efforts have focused on isolated molecules. 68 , 88 , 136 − 139 , 248 − 253 Here, we discuss two examples focused on crystalline materials.…”

Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study

“…Additionally, a total of 80 patients were defined in the prospective cohort as the test set. The ratio of the SCI group (n = 141) to the NO-SCI group(n = 105) in the training set was 1.34:1, indicating the absence of data imbalance issues 19 . Screening of characteristic indicators: Clinical characteristics and laboratory parameters with significant statistical differences were identified (p < 0.05) between groups through univariate analysis conducted in the training set.…”