A closer look into two-step perovskite conversion with X-ray scattering

Recently, hybrid perovskites have gathered much interest as alternative materials for the fabrication of highly efficient and cost-competitive solar cells; however, many questions regarding perovskite crystal formation and deposition methods remain. Here we have applied a two-step protocol where a crystalline PbI2 precursor film is converted to MAPbI3–xClx perovskite upon immersion in a mixed solution of methylammonium iodide and methylammonium chloride. We have investigated both films with grazing incidence small-angle X-ray scattering to probe the inner film morphology. Our results demonstrate a strong link between lateral crystal sizes in the films before and after conversion, which we attribute to laterally confined crystal growth. Additionally, we observe an accumulation of smaller grains within the bulk in contrast with the surface. Thus, our results help to elucidate the crystallization process of perovskite films deposited via a two-step technique that is crucial for controlled film formation, improved reproducibility, and high photovoltaic performance

Effect of chain architecture on the swelling and thermal response of star-shaped thermo-responsive (poly (methoxy diethylene glycol acrylate)-block-polystyrene)3 block copolymer films

The effect of chain architecture on the swelling and thermal response of thin films obtained from an amphiphilic three-arm star-shaped thermo-responsive block copolymer poly(methoxy diethylene glycol acrylate)-block-polystyrene ((PMDEGA-b-PS)3) is investigated by in situ neutron reflectivity (NR) measurements. The PMDEGA and PS blocks are micro-phase separated with randomly distributed PS nanodomains. The (PMDEGA-b-PS)3 films show a transition temperature (TT) at 33 °C in white light interferometry. The swelling capability of the (PMDEGA-b-PS)3 films in a D2O vapor atmosphere is better than that of films from linear PS-b-PMDEGA-b-PS triblock copolymers, which can be attributed to the hydrophilic end groups and limited size of the PS blocks in (PMDEGA-b-PS)3. However, the swelling kinetics of the as-prepared (PMDEGA-b-PS)3 films and the response of the swollen film to a temperature change above the TT are significantly slower than that in the PS-b-PMDEGA-b-PS films, which may be related to the conformation restriction by the star-shape. Unlike in the PS-b-PMDEGA-b-PS films, the amount of residual D2O in the collapsed (PMDEGA-b-PS)3 films depends on the final temperature. It decreases from (9.7 ± 0.3)% to (7.0 ± 0.3)% or (6.0 ± 0.3)% when the final temperatures are set to 35 °C, 45 °C and 50 °C, respectively. This temperature-dependent reduction of embedded D2O originates from the hindrance of chain conformation from the star-shaped chain architecture

Machine Learning for Molecular Dynamics on Long Timescales

Molecular dynamics (MD) simulation is widely used to analyze the properties of molecules and materials. Most practical applications, such as comparison with experimental measurements, designing drug molecules, or optimizing materials, rely on statistical quantities, which may be prohibitively expensive to compute from direct long-time MD simulations. Classical machine learning (ML) techniques have already had a profound impact on the field, especially for learning low-dimensional models of the long-time dynamics and for devising more efficient sampling schemes for computing long-time statistics. Novel ML methods have the potential to revolutionize long timescale MD and to obtain interpretable models. ML concepts such as statistical estimator theory, end-to-end learning, representation learning, and active learning are highly interesting for the MD researcher and will help to develop new solutions to hard MD problems. With the aim of better connecting the MD and ML research areas and spawning new research on this interface, we define the learning problems in long timescale MD, present successful approaches, and outline some of the unsolved ML problems in this application field