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Inorganic lead‐free halide perovskites, devoid of toxic or rare elements, have garnered considerable attention as photocatalysts for pollution control, CO2 reduction and hydrogen production. In the extensive perovskite design space, factors like substitution or doping level profoundly impact their performance. To address this complexity, a synergistic combination of machine learning models and theoretical calculations were used to efficiently screen substitution elements that enhanced the photoactivity of substituted Cs2AgBiBr6 perovskites. Machine learning models determined the importance of d10 orbitals, highlighting how substituent electron configuration affects electronic structure of Cs2AgBiBr6. Conspicuously, d10‐configured Zn2+ boosted the photoactivity of Cs2AgBiBr6. Experimental verification validated these model results, revealing a 13‐fold increase in photocatalytic toluene conversion compared to the unsubstituted counterpart. This enhancement resulted from the small charge carrier effective mass, as well as the creation of shallow trap states, shifting the conduction band minimum, introducing electron‐deficient Br, and altering the distance between the B‐site cations d band centre and the halide anions p band centre, a parameter tuneable through d10 configuration substituents. This study exemplifies the application of computational modelling in photocatalyst design and elucidating structure–property relationships. It underscores the potential of synergistic integration of calculations, modelling, and experimental analysis across various applications. [ABSTRACT FROM AUTHOR]

Organic–inorganic perovskite solar cells (PSCs) are promising candidates for next‐generation, inexpensive solar panels due to their commercially competitive cost and high power conversion efficiencies. However, PSCs suffer from poor stability. A new and vast subset of PSCs, quasi‐two‐dimensional Ruddlesden–Popper PSCs (quasi‐2D RP PSCs), has improved photostability and superior resilience to environmental conditions compared to three‐dimensional metal‐halide PSCs. To accelerate the search for new quasi‐2D RP PSCs, this work reports a combinatorial, machine learning (ML) enhanced high‐throughput perovskite film fabrication and optimization study. This work designs a bespoke experimental strategy and produces perovskite films with a range of different compositions using only spin‐coating free, reproducible robotic fabrication processes. The performance and characterization data of these solar cells are used to train a ML model that allow materials parameters to be optimized and direct the design of improved materials. The new, ML‐optimized, drop‐cast quasi‐2D RP perovskite films yield solar cells with power conversion efficiencies of up to 16.9%. [ABSTRACT FROM AUTHOR]

The functionalization of small‐molecule hole‐transport materials (HTMs) heavily relies on the rational design of molecular geometry, which can optimize both intrinsic HTM properties and interfacial properties for realizing high‐performance and stable lead halide perovskite solar cells (LHPSCs). Herein, two fluorene‐based donor–π linker–donor HTMs are seen, FL01 and FL02, whose side chains are tailored with planar phenyl‐carbazole groups and twisted triphenylamine groups, respectively. Benefiting from the high conformational flexibility of twisted side chains, the strong and oriented interaction via PbO bonding is well coordinated at the perovskite and FL02 interface, which favors the interfacial charge transfer as well as the protection of perovskite layer by effectively blocking or mitigating the diffusion of hygroscopic dopants toward the perovskite surface. Consequently, the performance of FL02 HTM‐based n–i–p LHPSCs is significantly enhanced by achieving a power conversion efficiency of 17.8%, which is twice higher than that (8.6%) of FL01 HTM‐based ones and comparable with the case (18.8%) of conventional spiro‐OMeTAD HTM‐based devices. More importantly, the FL02‐based devices exhibit impressively high operation and storage stabilities with T80 and TS80 lifetimes of >98 h and ≈270 days, respectively, which are among the longest lifespans for the type of hygroscopically doped LHPSCs. [ABSTRACT FROM AUTHOR]

Designing dopant‐free small‐molecule hole transport materials (HTMs) with self‐assembly behavior via noncovalent interactions is considered as one effective strategy to achieve high‐performance inverted perovskite solar cells (PSCs). Herein, two donor‐π bridge‐donor (D–π–D) HTMs are presented, TPASF and TPAOF, containing 3,6‐dimethoxythieno[3,2‐b]thiophene as a core part with 3‐fluoro‐N,N‐bis(4‐(methylthio)phenyl)aniline and 3‐fluoro‐N,N‐bis(4‐methoxyphenyl)aniline as side group. The synergistic F⋅⋅⋅S dipole–dipole intra‐ and intermolecular interactions in TPASF drive the self‐assembly of this molecule into a supramolecular nanofibrillar network, leading to high hole mobility, superior interfacial properties, and providing a good growth template for the perovskite layer atop. The corresponding dopant‐free TPASF‐based inverted devices exhibit a promising power conversion efficiency of 21.01% with a long T80 lifetime of ≈632 h under operational conditions. This work paves the way for the further development of new dopant‐free self‐assembled HTM designs for highly efficient and stable inverted PSCs. [ABSTRACT FROM AUTHOR]

The integration of a functional group into dopant‐free hole‐transport materials (HTMs) to modify the perovskite|HTM interface has become a promising strategy for high‐performance and stable perovskite solar cells (PSCs). In this work, a sulfonated phenothiazine‐based HTM is reported, namely TAS, which consists of a butterfly structure with a readily synthesized N,​N‐​bis[4‐​(methylthio)​phenyl]​aniline side functional group. The interaction between TAS and perovskite via Pb–S bond induces a dipole moment that deepens the valence band of perovskite and thereby leads to enhanced open‐circuit voltage in corresponding n‐i‐p PSCs. More importantly, the functionalization of perovskite surface via Pb–S bond promotes the hole extraction reaction while suppressing the interfacial non‐radiative recombination, contributing to a 20–50% performance improvement compared to less‐ (4‐​(methylthio)​‐​N‐​[4‐​(methylthio)​phenyl]​aniline, DAS) or non‐interacting (N,N‐bis(4‐methoxyphenyl)aniline, TAO) counterparts. Consequently, TAS‐based PSCs exhibit superior device stability with a high PCE retention (>90% of the initial value) after 125 days of storage in the air. [ABSTRACT FROM AUTHOR]

[Display omitted] • Halide perovskite phosphor with high quantum efficiency. • Machine learning model for material design. • Enhancing self-trapped exciton emission by tuning ion radius. • White light LED with commercial potential. The outstanding emission of halide perovskites make them ideal candidates for white emission light-emitting diodes (LEDs) for lighting applications. However, many perovskites contain toxic or scarce elements and have unsatisfactory stability. Here, we report a target-driven approach, based on active learning (AL) techniques, to discover halide perovskites suitable for commercial LED applications. Based on the similarity between halide and oxide perovskites, a model trained on an oxide perovskite dataset plus six AL-selected halide perovskites exhibited excellent performance for photoluminescence quantum yield (PLQY) predictions of oxide and halide perovskites. The model proposed a strong relationship between ionic radii and PLQY, postulated to be due to the self-trap excitons derived from the Jahn-Teller deformation. A novel halide perovskite phosphor, Cs 4 Zn(Bi 0.85 Sb 0.15) 2 Cl 12 :0.01Mn, was designed and synthesized with the aid of the model. It exhibited an 88 % PLQY and outstanding thermal and luminescent stability. A simple white LED was fabricated from this material, exemplifying its commercial potential. This study demonstrates how machine learning techniques can accelerate discovery of next-generation phosphors for high performance single emitter-based white-light emitting devices. [ABSTRACT FROM AUTHOR]