In the process of finding high-performance materials for organic photovoltaics (OPVs), it is meaningful if one can establish the relationship between chemical structures and photovoltaic properties even before synthesizing them. Here, we first establish a database containing over 1700 donor materials reported in the literature. Through supervised learning, our machine learning (ML) models can build up the structure-property relationship and, thus, implement fast screening of OPV materials. We explore several expressions for molecule structures, i.e., images, ASCII strings, descriptors, and fingerprints, as inputs for various ML algorithms. It is found that fingerprints with length over 1000 bits can obtain high prediction accuracy. The reliability of our approach is further verified by screening 10 newly designed donor materials. Good consistency between model predictions and experimental outcomes is obtained. The result indicates that ML is a powerful tool to prescreen new OPV materials, thus accelerating the development of the OPV field.

A novel nanomagnesium hydroxide powder and three kinds of micro-Mg(OH) 2 , with different particle sizes, were chosen as fillers and mixed with ethylene-propylene-diene monomer rubber (EPDM) to form a series of composites by a traditional rubber-processing technique. The results showed that the mechanical properties of composites improved with decreasing particle size. The nanocomposites were far stronger than the microcomposites, which also supported the view that rubber reinforcement requires nanoreinforcement. The effect of particle size on the fire resistance of composites was investigated by cone calorimetry and limiting oxygen index analysis, which showed that the particle size of powder had an impact on the fire resistance of composites. For the composites filled with untreated powder, the peak value of heat release rate decreased and T ign increased with decreasing particle size. In conclusion, the fire resistance of nanocomposites was better than that of microcomposites. Surface modification of particles sometimes substantially improved the mechanical properties of nanocomposites, but had no effect on either the mechanical properties of microcomposites or the fire resistance of nanocomposites and flame retardance.

Ionic thermoelectric (i-TE) material with mobile ions as charge carriers has the potential to generate large thermal voltages at low operating temperatures. This study highlights the role of ions in i-TE hydrogels employing a poly­(vinyl alcohol) (PVA) polymer matrix and a number of ion providers, e.g., KOH, KNO3, KCl, KBr, NaI, KI, and CsI. The relationship between the intrinsic physical parameters of the ion and the thermoelectric performance is established, indicating the ability to influence the hydrogen bond by the ion is a crucial factor. Among these i-TE hydrogels, the PVA/CsI hydrogel exhibits the largest ionic Seebeck coefficient, reaching 52.9 mV K–1, which is the largest of all i-TE materials reported to date. In addition, our work demonstrates the influence of ions on polymer configuration and provides an avenue for ion selection in the Soret effect in ionic thermoelectrics.

Aqueous zinc–ion batteries typically suffer from sluggish interfacial reaction kinetics and drastic cathode dissolution owing to the desolvation process of hydrated Zn2+ and continual adsorption/desorption behavior of water molecules, respectively. To address these obstacles, a bio‐inspired approach, which exploits the moderate metabolic energy of cell systems and the amphiphilic nature of plasma membranes, is employed to construct a bio‐inspired hydrophobic conductive poly(3,4‐ethylenedioxythiophene) film decorating α‐MnO2 cathode. Like plasma membranes, the bio‐inspired film can “selectively” boost Zn2+ migration with a lower energy barrier and maintain the integrity of the entire cathode. Electrochemical reaction kinetics analysis and theoretical calculations reveal that the bio‐inspired film can significantly improve the electrical conductivity of the electrode, endow the cathode–electrolyte interface with engineered hydrophobicity, and enhance the desolvation behavior of hydrated Zn2+. This results in an enhanced ion diffusion rate and minimized cathode dissolution, thereby boosting the overall interfacial reaction kinetics and cathode stability. Owing to these intriguing merits, the composite cathode can demonstrate remarkable cycling stability and rate performance in comparison with the pristine MnO2 cathode. Based on the bio‐inspired design philosophy, this work can provide a novel insight for future research on promoting the interfacial reaction kinetics and electrode stability for various battery systems.

Y6 and its derivatives have greatly improved the power conversion efficiency (PCE) of organic photovoltaics (OPVs). Further developing high‐performance Y6 derivative acceptor materials through the relationship between the chemical structures and properties of these materials will help accelerate the development of OPV. Here, machine learning and quantum chemistry are used to understand the structure–property relationships and develop new OPV acceptor materials. By encoding the molecules with an improved one‐hot code, the trained machine learning model shows good predictive performance, and 22 new acceptors with predicted PCE values greater than 17% within the virtual chemical space are screened out. Trends associated with the discovered high‐performing molecules suggest that Y6 derivatives with medium‐length side chains have higher performance. Further quantum chemistry calculations reveal that the end acceptor units mainly affect the frontier molecular orbital energy levels and the electrostatic potential on molecular surface, which in turn influence the performance of OPV devices. A series of promising Y6 derivative candidates is screened out and a rational design guide for developing high‐performance OPV acceptors is provided. The approach in this work can be extended to other material systems for rapid materials discovery and can provide a framework for designing novel and promising OPV materials.