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Excited-state properties of crystalline organic semiconductors are key to organic electronic device applications. Machine learning (ML) models capable of predicting these properties could significantly accelerate materials discovery. We use the sure-independence-screening-and-sparsifying-operator (SISSO) ML algorithm to generate models to predict the first singlet excitation energy, which corresponds to the optical gap, the first triplet excitation energy, the singlet–triplet gap, and the singlet exciton binding energy of organic molecular crystals. To train the models we use the “PAH101” dataset of many-body perturbation theory calculations within the GW approximation and Bethe–Salpeter equation (GW+BSE) for 101 crystals of polycyclic aromatic hydrocarbons (PAHs). The best performing SISSO models yield predictions within about 0.2 eV of the GW+BSE reference values. SISSO models are selected based on considerations of accuracy and computational cost to construct materials screening workflows for each property. The screening targets are chosen to demonstrate typical use-cases relevant for organic electronic devices. We show that the workflows based on SISSO models can effectively screen out most of the materials that are not of interest and significantly reduce the number of candidates selected for further evaluation using computationally expensive excited-state theory. 

Advertising transparency efforts aim to facilitate user knowledge and control over the use of their data for personalized advertising. We seek to understand the perceptions and use of ad transparency tools among U.S. adults and whether ad transparency information surfaced by such tools affects public perceptions of online surveillance. Results show that people are aware of, although not knowledgeable about, such tools and feel slightly satisfied with them but do not use them often. Drawing from the dataveillance effects in advertising landscape (DEAL) framework, we demonstrate that whereas exposure to no, less, or more extensive transparency information did not change perceived surveillance, exposure to more extensive targeting information increased knowledge about ad transparency efforts and self-efficacy in using transparency tools. Moreover, perceived surveillance is associated with perceived benefits and risks of personalized advertising, interest in ad transparency information, desire for privacy regulation, negative affect, and intention to protect privacy and use ad transparency tools. These relationships were moderated by privacy concern and privacy cynicism. Theoretically, this study offers an empirical test of key tenets of the DEAL framework and contributes to policy debates about ad transparency. 

Abstract The excited-state properties of molecular crystals are important for applications in organic electronic devices. TheGWapproximation and Bethe-Salpeter equation (GW+BSE) is the state-of-the-art method for calculating the excited-state properties of crystalline solids with periodic boundary conditions. We present the PAH101 dataset ofGW+BSE calculations for 101 molecular crystals of polycyclic aromatic hydrocarbons (PAHs) with up to  ~500 atoms in the unit cell. To the best of our knowledge, this is the firstGW+BSE dataset for molecular crystals. The data records include theGWquasiparticle band structure, the fundamental band gap, the static dielectric constant, the first singlet exciton energy (optical gap), the first triplet exciton energy, the dielectric function, and optical absorption spectra for light polarized along the three lattice vectors. The dataset can be used to (i) discover materials with desired electronic/optical properties, (ii) identify correlations between DFT andGW+BSE quantities, and (iii) train machine learned models to help in materials discovery efforts. 

The excited-state properties of molecular crystals are important for applications in organic electronic devices. The GW approximation and Bethe-Salpeter equation (GW+BSE) is the state-of-the-art method for calculating the excited-state properties of crystalline solids with periodic boundary conditions. We present the PAH101 dataset of GW +BSE calculations for 101 molecular crystals of polycyclic aromatic hydrocarbons (PAHs) with up to ∼500 atoms in the unit cell. The data records include the GW quasiparticle band structure, the fundamental band gap, the static dielectric constant, the first singlet exciton energy (optical gap), the first triplet exciton energy, the dielectric function, and optical absorption spectra for light polarized along the three lattice vectors. In addition, the dataset includes the density functional theory (DFT) single-molecule and crystal features used in Liu et al. [npj Computational Materials, 8, 70 (2022)]. We envision the dataset being used to (i) identify correlations between DFT and GW +BSE quantities, (ii) discover materials with desired electronic/ optical properties in the dataset itself, and (iii) train machine-learned models to help in materials discovery efforts. We provide examples to illustrate these three use cases. 

The excited-state properties of molecular crystals are important for applications in organic electronic devices. The GW approximation and Bethe-Salpeter equation (GW +BSE) is the state-of-the-art method for calculating the excited-state properties of crystalline solids with periodic boundary conditions. We present the PAH101 dataset of GW +BSE calculations for 101 molecular crystals of polycyclic aromatic hydrocarbons (PAHs) with up to ∼500 atoms in the unit cell. The data records include the GW quasiparticle band structure, the fundamental band gap, the static dielectric constant, the first singlet exciton energy (optical gap), the first triplet exciton energy, the dielectric function, and optical absorption spectra for light polarized along the three lattice vectors. In addition, the dataset includes the density functional theory (DFT) single-molecule and crystal features used in Liu et al. [npj Computational Materials, 8, 70 (2022)]. We envision the dataset being used to (i) identify correlations between DFT and GW +BSE quantities, (ii) discover materials with desired electronic/ optical properties in the dataset itself, and (iii) train machine-learned models to help in materials discovery efforts. We provide examples to illustrate these three use cases. 

This paper describes the results of the 2023 edition of the “LivDet” series of iris presentation attack detection (PAD) competitions. New elements in this fifth competition include (1) GAN-generated iris images as a category of presentation attack instruments (PAI), and (2) an evaluation of human accuracy at detecting PAI as a reference benchmark. Clarkson University and the University of Notre Dame contributed image datasets for the competition, composed of samples representing seven different PAI categories, as well as baseline PAD algorithms. Fraunhofer IGD, Beijing University of Civil Engineering and Architecture, and Hochschule Darmstadt contributed results for a total of eight PAD algorithms to the competition. Accuracy results are analyzed by different PAI types, and compared to human accuracy. Overall, the Fraunhofer IGD algorithm, using an attention-based pixel-wise binary supervision network, showed the best-weighted accuracy results (average classification error rate of 37.31%), while the Beijing University of Civil Engineering and Architecture’s algorithm won when equal weights for each PAI were given (average classification rate of 22.15%). These results suggest that iris PAD is still a challenging problem. 

Abstract Singlet fission (SF), the conversion of one singlet exciton into two triplet excitons, could significantly enhance solar cell efficiency. Molecular crystals that undergo SF are scarce. Computational exploration may accelerate the discovery of SF materials. However, many-body perturbation theory (MBPT) calculations of the excitonic properties of molecular crystals are impractical for large-scale materials screening. We use the sure-independence-screening-and-sparsifying-operator (SISSO) machine-learning algorithm to generate computationally efficient models that can predict the MBPT thermodynamic driving force for SF for a dataset of 101 polycyclic aromatic hydrocarbons (PAH101). SISSO generates models by iteratively combining physical primary features. The best models are selected by linear regression with cross-validation. The SISSO models successfully predict the SF driving force with errors below 0.2 eV. Based on the cost, accuracy, and classification performance of SISSO models, we propose a hierarchical materials screening workflow. Three potential SF candidates are found in the PAH101 set.