Metal halides perovskites, such as hybrid organic–inorganic CH3NH3PbI3, are newcomer optoelectronic materials that have attracted enormous attention as solution-deposited absorbing layers in solar cells with power conversion efficiencies reaching 20%. Herein we demonstrate a new avenue for halide perovskites by designing highly luminescent perovskite-based colloidal quantum dot materials. We have synthesized monodisperse colloidal nanocubes (4–15 nm edge lengths) of fully inorganic cesium lead halide perovskites (CsPbX3, X = Cl, Br, and I or mixed halide systems Cl/Br and Br/I) using inexpensive commercial precursors. Through compositional modulations and quantum size-effects, the bandgap energies and emission spectra are readily tunable over the entire visible spectral region of 410–700 nm. The photoluminescence of CsPbX3 nanocrystals is characterized by narrow emission line-widths of 12–42 nm, wide color gamut covering up to 140% of the NTSC color standard, high quantum yields of up to 90%, and radiative lifetimes in the range of 1–29 ns. The compelling combination of enhanced optical properties and chemical robustness makes CsPbX3 nanocrystals appealing for optoelectronic applications, particularly for blue and green spectral regions (410–530 nm), where typical metal chalcogenide-based quantum dots suffer from photodegradation.

The performance of organometallic perovskite solar cells has rapidly surpassed that of both conventional dye-sensitized and organic photovoltaics. High-power conversion efficiency can be realized in both mesoporous and thin-film device architectures. We address the origin of this success in the context of the materials chemistry and physics of the bulk perovskite as described by electronic structure calculations. In addition to the basic optoelectronic properties essential for an efficient photovoltaic device (spectrally suitable band gap, high optical absorption, low carrier effective masses), the materials are structurally and compositionally flexible. As we show, hybrid perovskites exhibit spontaneous electric polarization; we also suggest ways in which this can be tuned through judicious choice of the organic cation. The presence of ferroelectric domains will result in internal junctions that may aid separation of photoexcited electron and hole pairs, and reduction of recombination through segregation of charge carriers. The combination of high dielectric constant and low effective mass promotes both Wannier-Mott exciton separation and effective ionization of donor and acceptor defects. The photoferroic effect could be exploited in nanostructured films to generate a higher open circuit voltage and may contribute to the current–voltage hysteresis observed in perovskite solar cells.

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 (CH3NH3PbI3) 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.

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.