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The properties of semiconducting solids are determined by the imperfections they contain. Established physical phenomena can be converted into practical design principles for optimizing defects and doping in a broad range of technology-enabling materials.

While the theory of imperfections in solids is firmly established, procedures for first-principles calculations of defect quantities continue to evolve. A plethora of ad hoc correction schemes is being replaced by sophisticated self-consistent procedures that will enable more quantitative predictions of the formation energies of defect species and their spectroscopic signatures.

The vastness of chemical space presents a long-standing challenge for the exploration of new compounds with pre-determined properties. In materials science, crystal structure prediction has become a mature tool for mapping from composition to structure based on global optimisation techniques. Generative artificial intelligence now offers the means to efficiently navigate larger regions of crystal chemical space informed by structure-property datasets of materials. Here, we introduce a model, named Chemeleon, designed to generate chemical compositions and crystal structures by learning from both textual descriptions and three-dimensional structural data. The model employs denoising diffusion techniques for compound generation using textual inputs aligned with structural data via cross-modal contrastive learning. The potential of this approach is demonstrated for multi-component compound generation, including the Zn-Ti-O ternary space, and the prediction of stable phases in the Li-P-S-Cl quaternary space of relevance to solid-state batteries. The vastness of chemical space makes discovering new materials challenging. Here, authors propose a generative AI model enabling crystal structures generation from textual descriptions, accelerating material discovery.

Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence. Recent progress in machine learning in the chemical sciences and future directions in this field are discussed.

In principle, the induced chirality of hybrid perovskites results from symmetry-breaking within inorganic frameworks. However, the detailed mechanism behind the chirality transfer remains unknown due to the lack of systematic studies. Here, using the structural isomer with different functional group location, we deduce the effect of hydrogen-bonding interaction between two building blocks on the degree of chirality transfer in inorganic frameworks. The effect of asymmetric hydrogen-bonding interaction on chirality transfer was clearly demonstrated by thorough experimental analysis. Systematic studies of crystallography parameters confirm that the different asymmetric hydrogen-bonding interactions derived from different functional group location play a key role in chirality transfer phenomena and the resulting spin-related properties of chiral perovskites. The methodology to control the asymmetry of hydrogen-bonding interaction through the small structural difference of structure isomer cation can provide rational design paradigm for unprecedented spin-related properties of chiral perovskite. Chiral Ruddlesden-Popper perovskites are of interest as they exhibit circular dichroism in the visible light region. Here authors demonstrate the effect of asymmetric hydrogen bonding on the spin-polarization-based phenomena in chiral 2D perovskites by using structural isomer organic spacers.

Point defects in metal halide perovskites play a critical role in determining their properties and optoelectronic performance; however, many open questions remain unanswered. In this work, we apply impedance spectroscopy and deep-level transient spectroscopy to characterize the ionic defect landscape in methylammonium lead triiodide (MAPbI 3 ) perovskites in which defects were purposely introduced by fractionally changing the precursor stoichiometry. Our results highlight the profound influence of defects on the electronic landscape, exemplified by their impact on the device built-in potential, and consequently, the open-circuit voltage. Even low ion densities can have an impact on the electronic landscape when both cations and anions are considered as mobile. Moreover, we find that all measured ionic defects fulfil the Meyer–Neldel rule with a characteristic energy connected to the underlying ion hopping process. These findings support a general categorization of defects in halide perovskite compounds. Defects in perovskite affect the properties and performance in optoelectronic devices, yet the nature of ionic defects remains elusive. Here, the authors investigate the ionic defect landscape in perovskite introduced by varying precursor stoichiometry, and find the defects fulfill the Meyer-Neldel rule.

Solar cells based on organic–inorganic halide perovskites have recently shown rapidly rising power conversion efficiencies, but exhibit unusual behaviour such as current–voltage hysteresis and a low-frequency giant dielectric response. Ionic transport has been suggested to be an important factor contributing to these effects; however, the chemical origin of this transport and the mobile species are unclear. Here, the activation energies for ionic migration in methylammonium lead iodide (CH 3 NH 3 PbI 3 ) are derived from first principles, and are compared with kinetic data extracted from the current–voltage response of a perovskite-based solar cell. We identify the microscopic transport mechanisms, and find facile vacancy-assisted migration of iodide ions with an activation energy of 0.6 eV, in good agreement with the kinetic measurements. The results of this combined computational and experimental study suggest that hybrid halide perovskites are mixed ionic–electronic conductors, a finding that has major implications for solar cell device architectures. Understanding the mechanism of ionic transport in organic–inorganic halide perovskites is crucial for the design of future solar cells. Here, Eames et al. undertake a combined experimental and computational study to elucidate the ion conducting species and help rationalize the unusual behaviour observed in these perovskite-based devices.

The optoelectronic performance of wide-bandgap semiconductors often cannot compete with that of their defect-tolerant small-bandgap counterpart. Here, the authors outline three main challenges to overcome for mitigating the impact of defects in wide-bandgap semiconductors.

Control of defect processes in photovoltaic materials is essential for realizing high-efficiency solar cells and related optoelectronic devices. Native defects and extrinsic dopants tune the Fermi level and enable semiconducting p–n junctions; however, fundamental limits to doping exist in many compounds. Optical transitions from defect states can enhance photocurrent generation through sub-bandgap absorption; however, these defect states are also often responsible for carrier trapping and non-radiative recombination events that limit the voltage in operating solar cells. Many classes of materials, including metal oxides, chalcogenides and halides, are being examined for next-generation solar energy applications, and each technology faces distinct challenges that could benefit from point defect engineering. Here, we review the evolution in the understanding of point defect behaviour from Si-based photovoltaics to thin-film CdTe and Cu(In,Ga)Se 2 technologies, through to the latest generation of halide perovskite (CH 3 NH 3 PbI 3 ) and kesterite (Cu 2 ZnSnS 4 ) devices. We focus on the chemical bonding that underpins the defect chemistry and the atomistic processes associated with the photophysics of charge-carrier generation, trapping and recombination in solar cells. Finally, we outline general principles to enable defect control in complex semiconducting materials. Point defects have a key role in determining the performance of photovoltaic materials. In this Review, we assess defect processes in a range of photovoltaic materials and outline how point defect engineering could be used to improve the efficiency of solar cells.

Chiral perovskites are being extensively studied as a promising candidate for spintronic- and polarization-based optoelectronic devices due to their interesting spin-polarization properties. However, the origin of chiroptical activity in chiral perovskites is still unknown, as the chirality transfer mechanism has been rarely explored. Here, through the nano-confined growth of chiral perovskites (MBA 2 PbI 4(1-x) Br 4x ), we verified that the asymmetric hydrogen-bonding interaction between chiral molecular spacers and the inorganic framework plays a key role in promoting the chiroptical activity of chiral perovskites. Based on this understanding, we observed remarkable asymmetry behavior (absorption dissymmetry of 2.0 × 10 −3 and anisotropy factor of photoluminescence of 6.4 × 10 −2 for left- and right-handed circularly polarized light) in nanoconfined chiral perovskites even at room temperature. Our findings suggest that electronic interactions between building blocks should be considered when interpreting the chirality transfer phenomena and designing hybrid materials for future spintronic and polarization-based devices. In this study, Ma et al. demonstrated that asymmetric hydrogen-bonding interaction between chiral organic spacer and inorganic frameworks plays a key role in promoting the chiroptical activity of chiral perovskites.