No abstract

Metal halide perovskites (MHPs) have gained considerable attention due to their excellent optoelectronic performance, which is often attributed to unusual defect properties. We demonstrate that midgap defect levels can exhibit very large and slow energy fluctuations associated with anharmonic acoustic motions. Therefore, care should be taken classifying MHP defects as deep or shallow, since shallow defects may become deep and vice versa. As a consequence, charges from deep levels can escape into bands, and light absorption can be extended to longer wavelengths, improving material performance. The phenomenon, demonstrated with iodine vacancy in CH 3 NH 3 PbI 3 using a machine learning force field, can be expected for a variety of defects and dopants in many MHPs and other soft inorganic semiconductors. Since large-scale anharmonic motions can be precursors to chemical decomposition, a known problem with MHPs, we propose that materials that are stiffer than MHPs but softer than traditional inorganic semiconductors, such as Si and TiO 2 , may simultaneously exhibit excellent performance and stability.

Nonadiabatic (NA) molecular dynamics (MD) allows one to study farfrom-equilibrium processes involving excited electronic states coupled to atomic motions. While NAMD involves expensive calculations of excitation energies and NA couplings (NACs), ground-state properties require much less effort and can be obtained with machine learning (ML) at a fraction of the ab initio cost. Application of ML to excited states and NACs is more challenging, due to costly reference methods, many states, and complex geometry dependence. We developed a NAMD methodology that avoids time extrapolation of excitation energies and NACs. Instead, under the classical path approximation that employs a precomputed ground-state trajectory, we use a small fraction (2%) of the geometries to train neural networks and obtain excited-state energies and NACs for the remaining 98% of the geometries by interpolation. Demonstrated with metal halide perovskites that exhibit complex MD, the method provides nearly two orders of computational savings while generating accurate NAMD results.

Under natural conditions, plants are exposed to various abiotic and biotic stresses that trigger rapid changes in the production and removal of reactive oxygen species (ROS) such as hydrogen peroxide (H2O2). The ascorbate-glutathione pathway has been recognized to be a key player in H2O2 metabolism, in which reduced glutathione (GSH) regenerates ascorbate by reducing dehydroascorbate (DHA), either chemically or via DHA reductase (DHAR), an enzyme belonging to the glutathione S-transferase (GST) superfamily. Thus, DHAR has been considered to be important in maintaining the ascorbate pool and its redox state. Although some GSTs and peroxiredoxins may contribute to GSH oxidation, analysis of Arabidopsis dhar mutants has identified the key role of DHAR in coupling H2O2 to GSH oxidation. The reaction of DHAR has been proposed to proceed by a ping-pong mechanism, in which binding of DHA to the free reduced form of the enzyme is followed by binding of GSH. Information from crystal structures has shed light on the formation of sulfenic acid at the catalytic cysteine of DHAR that occurs with the reduction of DHA. In this review, we discuss the molecular properties of DHAR and its importance in coupling the ascorbate and glutathione pools with H2O2 metabolism, together with its functions in plant defense, growth, and development.

All-inorganic perovskites are promising candidates for solar energy and optoelectronic applications, despite their polycrystalline nature with a large density of grain boundaries (GBs) due to facile solution-processed fabrication. GBs exhibit complex atomistic structures undergoing slow rearrangements. By studying evolution of the Σ5(210) CsPbBr 3 GB on a nanosecond time scale, comparable to charge carrier lifetimes, we demonstrate that GB deformations appear every ∼100 ps and increase significantly the probability of deep charge traps. However, the deep traps form only transiently for a few hundred femtoseconds. In contrast, shallow traps appear continuously at the GB. Shallow traps are localized in the GB layer, while deep traps are in a sublayer, which is still distorted from the pristine structure and can be jammed in unfavorable conformations. The GB electronic properties correlate with bond angles, with notable exception of the Br−Br distance, which provides a signature of halide migration along GBs. The transient nature of trap states and localization of electrons and holes at different parts of GBs indicate that charge carrier lifetimes should be long. At the same time, charge mobility can be reduced. The complex, multiscale evolution of geometric and electronic structures of GBs rationalize the contradictory statements made in the literature regarding both benign and detrimental roles of GBs in perovskite performance and provide new atomistic insights into perovskite properties.

Nonadiabatic (NA) molecular dynamics (MD) allows one to investigate far-from-equilibrium processes in nanoscale and molecular materials at the atomistic level and in the time domain, mimicking time-resolved spectroscopic experiments. Ab initio NAMD is limited to about 100 atoms and a few picoseconds, due to computational cost of excitation energies and NA couplings. We develop a straightforward methodology that can extend ab initio quality NAMD to nanoseconds and thousands of atoms. The ab initio NAMD Hamiltonian is sampled and interpolated along a trajectory using a Fourier transform, and then, it is used to perform NAMD with known algorithms. The methodology relies on the classical path approximation, which holds for many materials and processes. To achieve a complete ab initio quality description, the trajectory can be obtained using an ab initio trained machine learning force field. The method is demonstrated with charge carrier trapping and relaxation in hybrid organic–inorganic and all-inorganic metal halide perovskites that exhibit complex dynamics and are actively studied for optoelectronic applications.

Using supervised and unsupervised machine learning (ML) on features generated from nonadiabatic (NA) molecular dynamics (MD) trajectories under the classical path approximation, we demonstrate that mutual information with the NA Hamiltonian can be used for feature selection and model simplification. Focusing on CsPbI 3 , a popular metal halide perovskite, we observe that the chemical environment of a single element is sufficient for predicting the NA Hamiltonian. The conclusion applies even to Cs, although Cs does not contribute to the relevant wave functions. Interatomic distances between Cs and I or Pb and the octahedral tilt angle are the most important features. We reduce a typical 360-parameter ML force-field model to just a 12-parameter NA Hamiltonian model, while maintaining a high NA-MD simulation quality. Because NA-MD is a valuable tool for studying excited state processes, overcoming its high computational cost through simple ML models will streamline NA-MD simulations and expand the ranges of accessible system size and simulation time.

Metal halide perovskites (MHPs) have attracted attention because of their high optoelectronic performance that is fundamentally rooted in the unusual properties of MHP defects. By developing an ab initio-based machine-learning force field, we sample the structural dynamics of MHPs on a nanosecond time scale and show that halide vacancies create midgap trap states in the MHP bulk but not on a surface. Deep traps result from Pb−Pb dimers that can form across the vacancy in only the bulk. The required shortening of the Pb−Pb distance by nearly 3 Å is facilitated by either charge trapping or 50 ps thermal fluctuations. The large-scale structural deformations are possible because MHPs are soft. Halide vacancies on the MHP surface create no deep traps but separate electrons from holes, keeping the charges mobile. This is particularly favorable for MHP quantum dots, which do not require sophisticated surface passivation to emit light and blink less than quantum dots formed from traditional inorganic semiconductors.