Large-crystal perovskite films: The performance of organic-inorganic hybrid perovskite planar solar cells has steadily improved. One outstanding issue is that grain boundaries and defects in polycrystalline films degrade their output. Now, two studies report the growth of millimeter-scale single crystals. Nie et al. grew continuous, pinhole-free, thin iodochloride films with a hot-casting technique and report device efficiencies of 18%. Shi et al. used antisolvent vapor-assisted crystallization to grow millimeter-scale bromide and iodide cubic crystals with charge-carrier diffusion lengths exceeding 10 mm. Science , this issue p. 522 , p. 519

How perovskites have the edge: Two-dimensional Ruddlesden-Popper perovskites form quantum wells by sandwiching inorganic-organic perovskite layers used in photovoltaic devices between organic layers. Blancon et al. show that if the perovskite layer is more than two unit cells thick, photogenerated excitons undergo an unusual but highly efficient process for creating free carriers that can be harvested in photovoltaic devices (see the Perspective by Bakr and Mohammed). Lower-energy local states at the edges of the perovskite layer facilitate dissociation into electrons and holes that are well protected from recombination. Science , this issue p. 1288 ; see also p. 1260

Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist’s toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy but are computationally expensive and scale poorly to large systems, while classical force fields are cheap and scalable, but lack transferability to new systems. Machine learning can be used to achieve the best of both approaches. Here we train a general-purpose neural network potential (ANI-1ccx) that approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions. This is achieved by training a network to DFT data then using transfer learning techniques to retrain on a dataset of gold standard QM calculations (CCSD(T)/CBS) that optimally spans chemical space. The resulting potential is broadly applicable to materials science, biology, and chemistry, and billions of times faster than CCSD(T)/CBS calculations.

Ruddlesden–Popper halide perovskites are 2D solution-processed quantum wells with a general formula A₂A’_n-1M _n X_3n+1, where optoelectronic properties can be tuned by varying the perovskite layer thickness (n-value), and have recently emerged as efficient semiconductors with technologically relevant stability. However, fundamental questions concerning the nature of optical resonances (excitons or free carriers) and the exciton reduced mass, and their scaling with quantum well thickness, which are critical for designing efficient optoelectronic devices, remain unresolved. Here, using optical spectroscopy and 60-Tesla magneto-absorption supported by modeling, we unambiguously demonstrate that the optical resonances arise from tightly bound excitons with both exciton reduced masses and binding energies decreasing, respectively, from 0.221 m₀ to 0.186 m₀ and from 470 meV to 125 meV with increasing thickness from n equals 1 to 5. Based on this study we propose a general scaling law to determine the binding energy of excitons in perovskite quantum wells of any layer thickness.

Light relaxes hybrid perovskites: Ion migration in organic-inorganic perovskite solar cells limits device stability and performance. Tsai et al. found that a cesium-doped lead triiodide perovskite with mixed organic cations underwent a uniform lattice expansion after 180 min of exposure at 1 sun of illumination. This structural change reduced the energy barriers for charge carriers at the contacts of solar cells. The resulting increase in power conversion efficiency from 18.5 to 20.5% was maintained for more than 1500 hours of illumination. Science , this issue p. 67