Suppressing nucleation over large areas: Although formamidinium-based lead iodide (PbI ₂ ) perovskites have a favorable bandgap and good thermal stability, the difficulty in controlling nucleation makes it difficult to grow high-quality, large-area films compared with methylammonium counterparts. Bu et al. show that adding N -methyl-2-pyrrolidone to the perovskite precursors forms an adduct with PbI ₂ that promotes the formation of the desired black α-phase at room temperature. The addition of potassium hexafluorophosphate eliminated hysteresis by passivating interfacial defects and promoted long-term thermal stability at 85°C in unencapsulated devices. Large-area modules (17 square centimeters) achieved power conversion efficiencies of 20.4%. Science , abh1035, this issue p. 1327

The curse of dimensionality is commonly encountered in numerical partial differential equations (PDE), especially when uncertainties have to be modelled into the equations as random coefficients. However, very often the variability of physical quantities derived from PDE can be captured by a few features on the space of the coefficient fields. Based on such observation, we propose using neural network to parameterise the physical quantity of interest as a function of input coefficients. The representability of such quantity using a neural network can be justified by viewing the neural network as performing time evolution to find the solutions to the PDE. We further demonstrate the simplicity and accuracy of the approach through notable examples of PDEs in engineering and physics.

This paper establishes the optimal approximation error characterization of deep rectified linear unit (ReLU) networks for smooth functions in terms of both width and depth simultaneously. To that e...

Markov state models (MSMs) have become the tool of choice to analyze large amounts of molecular dynamics data by approximating them as a Markov jump process between suitably predefined states. Here we investigate “Core Set MSMs,” a new type of MSMs that build on metastable core sets acting as milestones for tracing the rare event kinetics. We present a thorough analysis of Core Set MSMs based on the existing milestoning framework, Bayesian estimation methods and Transition Path Theory (TPT). We show that Core Set MSMs can be used to extract phenomenological rate constants between the metastable sets of the system and to approximate the evolution of certain key observables. The performance of Core Set MSMs in comparison to standard MSMs is analyzed and illustrated on a toy example and in the context of the torsion angle dynamics of alanine dipeptide.