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Machine learning (ML) is effective to establish the complicated trilateral relationship among structures, properties, and photovoltaic performance, which is fundamental issue in developing novel materials for improving power conversion efficiency (PCE) of organic solar cells (OSCs). Herein, we constructed the database of 397 donor–acceptor pairs of OSCs with photovoltaic parameters and descriptor sets, which include donor–acceptor weight ratio within the active layer of the OSCs, root mean square of roughness, and 1024-bit Morgan molecular fingerprint for donor (Fp-D) and acceptor (Fp-A). The ML models random forest (RF), adaptive boosting (AdaBoost), extra trees regression, and gradient boosting regression trees were trained based on the descriptor set. The metrics determination coefficient (R2), Pearson correlation coefficient (r), root mean square error, and mean absolute error were selected to evaluate ML model performances. The results showed that the RF model exhibits the highest accuracy and stability for PCE prediction among these four ML models. Moreover, based on the decomposition of non-fullerene acceptors L8-BO, BTP-ec9, AQx-2, and IEICO, 20 acceptor molecules with symmetric A–D–A and A–π–D–π–A architectures were designed. The photovoltaic parameters of the designed acceptors were predicted using the trained RF model, and the virtual screening of designed acceptors was conducted based on the predicted PCE. The results indicate that six designed acceptors can reach the predicted PCE higher than 12% when P3HT was adopted as a donor. While PM6 was applied as a donor, five designed acceptors can achieve the predicted PCE higher than 16%. [ABSTRACT FROM AUTHOR]

The complicated trilateral relationships among molecular structures, properties, and photovoltaic performances of electron donor and acceptor materials hinder the rapid improvement of power conversion efficiency (PCE) of organic solar cells (OSCs). Herein, the database of 310 donor and non‐fullerene acceptor pairs is constructed and 39 molecular structure descriptors are selected. Four kinds of machine learning (ML) algorithms random forest (RF), extra trees regression, gradient boosting regression trees, and adaptive boosting are applied to predict photovoltaic parameters. The coefficient of determination, Pearson correlation coefficient, mean absolute error, and root mean square error are adopted to evaluate ML performance. The results show that the RF model exhibits the best prediction accuracy. The Gini important analysis suggests the fused ring and aromatic heterocycles are critical fragments in determining PCE. The molecular unit sets are constructed by cutting each donor and acceptor molecules in database. The 31 752 D‐π‐A‐π type donor molecules and 5 455 164 A‐π‐D‐π‐A type acceptor molecules are designed by recombination of molecular units, and 173 212 367 328 donor–acceptor pairs are generated by combining the newly designed donor and acceptor molecules. Based on the predicted PCE using the trained RF model, 42 donor–acceptor pairs exhibit the predicted PCE > 16%, in which the highest PCE is 16.24%. [ABSTRACT FROM AUTHOR]

Most previously reported Dirac valley degeneracies in continuous phononic crystal plates originate from Bragg scattering of the structures and generally have only single-band elastic-wave topological edge states. In the present work, a pair of triangular prisms is used in the construction of hexagonal-lattice phononic crystal plates to mimic the dual-band elastic valley Hall effect. Based on the spatial inversion symmetry conditions, which are related to the intrinsic frequencies of the resonators, the valley degeneracies, topological nontrivial bandgaps, and energy band inversion characteristics of multiple resonance modes are investigated by using the finite element method. Edge passbands combining distinct topology phases exist in each of the two nontrivial bandgaps of the ribbon configuration. The full-field simulations for flexural waves in the waveguide structure are demonstrated to support topologically valley-protected edge transmission in both bands, which immunizes the transport against backscattering from large corners and defects in the route. This work provides a reference for valley edge protection in subwavelength continuous elastic plate media and for the manipulation of the elastic waves at multiple frequencies. [ABSTRACT FROM AUTHOR]

Based on the analogy of the quantum valley Hall effect, a ligament-type phononic crystal plate with local resonators is designed in this study to facilitate the valley state transport of low-frequency elastic waves. We analyze the key factors affecting the local resonance modes and reduce the frequency of the Dirac cone by changing the connection form of the structure's beams. The spatial inversion symmetry of the structure is broken to open a new band gap by introducing a mass difference in the resonator pair. The robustness of the designed structure's topological valley waveguide under defects and bends is verified. Based on this characteristic, we introduce the gradient heights into the supercell structure where elastic waves at different frequencies split and stop significantly on the supercell structure to achieve sub-wavelength topological rainbow trapping. This design provides a theoretical reference for exploring the low-frequency elastic topological mode and the application of topological rainbow capture in sub-wavelength structures. [ABSTRACT FROM AUTHOR]

The selection of electron donors and nonfullerene acceptors (NFAs) in organic solar cells (OSCs) is crucial for improving photovoltaic performance. Machine learning (ML) has brought a breakthrough solution. Herein, 292 donor‐NFA pairs with experimental OSC parameters from the reported articles are collected. The ML descriptors include device processing parameters, molecular properties, and molecular structure. The five ML regression models, random forest (RF), extra tree regression, gradient boosting regression tree, adaptive boosting, and artificial neural network (ANN) are trained. GridSearchCV is used for hyperparameter optimization of ML regression models. The SHapley Additive exPlanation approach is employed to analyze descriptor importance. Among the trained five ML models, the RF model shows superior performance, achieving Pearson's correlation coefficient (r) of 0.81 on the test set. Based on the donors and NFAs in constructed dataset, the 9779 donor–NFA pairs for OSCs are generated by randomly combining donor and acceptor molecules. The trained RF model is utilized to predict the power conversion efficiency (PCE) of new donor–acceptor pairs for OSCs. The results indicate that the OSC composed of PBDB‐TF as donor and L8‐BO as acceptor can achieve the remarkable PCE of 17.9%. [ABSTRACT FROM AUTHOR]

The modification of fused rings in the central backbone, side chains, and end groups of the non‐fullerene acceptor (NFA) for organic solar cells (OSCs) can modulate properties and photovoltaic performance. In order to investigate the effect of fused rings and side chains on photovoltaic performance, PBDB‐T is selected as electron donor and IDTIC, IDIC, IDIC‐PhC6, IDIC‐C4Ph, 4TIC, ITIC‐OE, IDTTIC, ITC6‐IC, ITIC, and C8‐ITIC are selected as NFAs. Based on quantum chemistry calculations, the geometries, electronic structures, excitation properties, excited‐state lifetimes, and electrostatic potentials (ESPs) of the monomer and the PBDB‐T:NFA complexes are studied, and the rate constants of charge transfer (CT), exciton dissociation (ED) and charge recombination (CR) processes are analyzed. The results show that the increase of fused‐ring in NFA's backbone by substituting phenyl or thienyl with bithiophene can elevate the highest occupied molecular orbital energies, reduce the gap of frontier molecular orbital energies, induce red‐shift of absorption spectrum, increase CT energy, improve CT and ED rates. This work provides a detailed understanding of tuning optoelectronic properties and photovoltaic performance by modifying NFA's side chains and extending backbones. [ABSTRACT FROM AUTHOR]

The exploration of the physical attributes of the recently discovered orthocarbonate Sr3CO5 is significant for comprehending the carbon cycle and storage mechanisms within the Earth's interior. In this study, first-principles calculations are initially used to examine the structural phase transitions of Sr3CO5 polymorphs within the range of lower mantle pressures. The results suggest that Sr3CO5 with the Cmcm phase exhibits a minimal enthalpy between 8.3 and 30.3 GPa. As the pressure exceeds 30.3 GPa, the Cmcm phase undergoes a transition to the I4/mcm phase, while the experimentally observed Pnma phase remains metastable under our studied pressure. Furthermore, the structural data of SrO, SrCO3, and Sr3CO5 polymorphs are utilized to develop a deep learning potential model suitable for the Sr–C–O system, and the pressure–volume relationship and elastic constants calculated using the potential model are in line with the available results. Subsequently, the elastic properties of Cmcm and I4/mcm phases in Sr3CO5 at high temperature and pressure are calculated using the molecular dynamics method. The results indicate that the I4/mcm phase exhibits higher temperature sensitivity in terms of elastic moduli and wave velocities compared to the Cmcm phase. Finally, the thermodynamic properties of the Cmcm and I4/mcm phases are predicted in the range of 0–2000 K and 10–120 GPa, revealing that the heat capacity and bulk thermal expansion coefficient of both phases increase with temperature, with the constant volume heat capacity gradually approaching the Dulong–Petit limit as the temperature rises. [ABSTRACT FROM AUTHOR]