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Abstract Although all‐inorganic metal halide perovskites (MHPs) have shown tremendous improvement, they are still inferior to the hybrid organic–inorganic MHPs in efficiency. Recently, a conceptually new β‐CsPbI₃perovskite reached 18.4 % efficiency combined with good thermodynamic stability at ambient conditions. We use ab initio non‐adiabatic molecular dynamics to show that native point defects in β‐CsPbI₃are generally benign for nonradiative charge recombination, regardless of whether they introduce shallow or deep trap states. These results indicate that MHPs do not follow the simple models used to explain defect‐mediated charge recombination in the conventional semiconductors. The strong tolerance is due to the softness of the perovskite lattice, which permits separation of electrons and holes upon defect formation, and only allows carriers to couple to the low‐frequency vibrations. Both factors decrease notably the non‐adiabatic coupling and slow down the dissipation of energy to heat. 

The stabilization of supported nanoclusters is critical for different applications, including catalysis and plasmonics. Herein we investigate the impact of MoS 2 grain boundaries (GBs) on the nucleation and growth of Pt NCs. The optimum atomic structure of the metal clusters is obtained using an adaptive genetic algorithm that employs a hybrid approach based on atomistic force fields and density functional theory. Our findings show that GBs stabilize the NCs up to a cluster size of nearly ten atoms, and with larger clusters having a similar binding to the pristine system. Notably, Pt monomers are found to be attracted to GB cores achieving 60% more stabilization compared to the pristine surface. Furthermore, we show that the nucleation and growth of the metal seeds are facile with low kinetic barriers, which are of similar magnitude to the diffusion barriers of metals on the pristine surface. The findings highlight the need to engineer ultrasmall NCs to take advantage of enhanced stabilization imparted by the GB region, particularly to circumvent sintering behavior for high-temperature applications. 

Nørskov and collaborators proposed a simple kinetic model to explain the volcano relation for the hydrogen evolution reaction on transition metal surfaces such that j 0 = k 0 f (Δ G H ) where j 0 is the exchange current density, f (Δ G H ) is a function of the hydrogen adsorption free energy Δ G H as computed from density functional theory, and k 0 is a universal rate constant. Herein, focusing on the hydrogen evolution reaction in acidic medium, we revisit the original experimental data and find that the fidelity of this kinetic model can be significantly improved by invoking metal-dependence on k 0 such that the logarithm of k 0 linearly depends on the absolute value of Δ G H . We further confirm this relationship using additional experimental data points obtained from a critical review of the available literature. Our analyses show that the new model decreases the discrepancy between calculated and experimental exchange current density values by up to four orders of magnitude. Furthermore, we show the model can be further improved using machine learning and statistical inference methods that integrate additional material properties. 

We develop an ab initio nonadiabatic molecular dynamics (NAMD) method based on GW plus real-time Bethe-Salpeter equation ( GW + rtBSE-NAMD) for the spin-resolved exciton dynamics. From investigations on MoS 2 , we provide a comprehensive picture of spin-valley exciton dynamics where the electron-phonon (e-ph) scattering, spin-orbit interaction (SOI), and electron-hole (e-h) interactions come into play collectively. In particular, we provide a direct evidence that e-h exchange interaction plays a dominant role in the fast valley depolarization within a few picoseconds, which is in excellent agreement with experiments. Moreover, there are bright-to-dark exciton transitions induced by e-ph scattering and SOI. Our study proves that e-h many-body effects are essential to understand the spin-valley exciton dynamics in transition metal dichalcogenides and the newly developed GW + rtBSE-NAMD method provides a powerful tool for exciton dynamics in extended systems with time, space, momentum, energy, and spin resolution. 

Abstract The development of statistical tools based on machine learning (ML) and deep networks is actively sought for materials design problems. While structure-property relationships can be accurately determined using quantum mechanical methods, these first-principles calculations are computationally demanding, limiting their use in screening a large set of candidate structures. Herein, we use convolutional neural networks to develop a predictive model for the electronic properties of metal halide perovskites (MHPs) that have a billions-range materials design space. We show that a well-designed hierarchical ML approach has a higher fidelity in predicting properties of the MHPs compared to straight-forward methods. In this architecture, each neural network element has a designated role in the estimation process from predicting complex features of the perovskites such as lattice constant and octahedral till angle to narrowing down possible ranges for the values of interest. Using the hierarchical ML scheme, the obtained root-mean-square errors for the lattice constants, octahedral angle and bandgap for the MHPs are 0.01 Å, 5°, and 0.02 eV, respectively. Our study underscores the importance of a careful network design and a hierarchical approach to alleviate issues associated with imbalanced dataset distributions, which is invariably common in materials datasets.