Dimensionality reduction in machine learning for nonadiabatic molecular dynamics: Effectiveness of elemental sublattices in lead halide perovskites.

Supervised machine learning (ML) and unsupervised ML have been performed on descriptors generated from nonadiabatic (NA) molecular dynamics (MD) trajectories representing non-radiative charge recombination in CsPbI3, a promising solar cell and optoelectronic material. Descriptors generated from every third atom of the iodine sublattice alone are sufficient for a satisfactory prediction of the bandgap and NA coupling for the use in the NA-MD simulation of nonradiative charge recombination, which has a strong influence on material performance. Surprisingly, descriptors based on the cesium sublattice perform better than those of the lead sublattice, even though Cs does not contribute to the relevant wavefunctions, while Pb forms the conduction band and contributes to the valence band. Simplification of the ML models of the NA-MD Hamiltonian achieved by the present analysis helps to overcome the high computational cost of NA-MD through ML and increase the applicability of NA-MD simulations.

Importation of taxadiene synthase into chloroplast improves taxadiene production in tobacco.

MAIN CONCLUSION: Importation of taxadiene synthase into chloroplasts is important for the efficient heterologous production of taxadiene. Taxadiene, the first committed precursor to taxol, is synthesized from geranylgeranyl pyrophosphate (GGPP) by action of taxadiene synthase (TS). Heterologous production of taxadiene could potentially rely on both cytosolic mevalonic acid (MVA) pathway and the plastidic methylerythritol phosphate (MEP) pathway. We suggest the compartmentalized engineering in chloroplast as an efficient approach for taxadiene production. In this study, we directly introduced the TS gene from Taxus brevifolia into the tobacco chloroplast genome and found that the transplastomic plants accumulated a low content of taxadiene, ~ 5.6 µg/g dry weight (DW). Moreover, we tried a combination of MEP and MVA pathways for taxadiene synthesis by nuclear transformation with a truncated version of TS (without encoding a transit peptide) into the transplastomic plants. However, this did not further improve the taxadiene production. In contrast, we found that taxadiene could be produced up to 87.8 µg/g DW in leaves of transgenic plants expressing TS with a chloroplast transit peptide, which was significantly higher than that in leaves of transplastomic plants. Thus, this study highlights the importance of TS importation into chloroplast for production of taxadiene.

The pivotal function of dehydroascorbate reductase in glutathione homeostasis in plants.

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.

Photocatalytic activity of dual defect modified graphitic carbon nitride is robust to tautomerism: machine learning assisted ab initio quantum dynamics.

Two-dimensional graphitic carbon nitride (GCN) is a popular metal-free polymer for sustainable energy applications due to its unique structure and semiconductor properties. Dopants and defects are used to tune GCN, and dual defect modified GCN exhibits superior properties and enhanced photocatalytic efficiency in comparison to pristine or single defect GCN. We employ a multistep approach combining time-dependent density functional theory and nonadiabatic molecular dynamics (NAMD) with machine learning (ML) to investigate coupled structural and electronic dynamics in GCN over a nanosecond timescale, comparable to and exceeding the lifetimes of photo-generated charge carriers and photocatalytic events. Although frequent hydrogen hopping transitions occur among four tautomeric structures, the electron-hole separation and recombination processes are only weakly sensitive to the tautomerism. The charge separated state survives for about 10 ps, sufficiently long to enable photocatalysis. The employed ML-NAMD methodology provides insights into rare events that can influence excited state dynamics in the condensed phase and nanoscale materials and extends NAMD simulations from pico- to nanoseconds. The ab initio quantum dynamics simulation provides a detailed atomistic mechanism of photoinduced evolution of charge carriers in GCN and rationalizes how GCN remains photo-catalytically active despite its multiple isomeric and tautomeric forms.

Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Bidirectional Long Short-Term Memory Networks.

Essential for understanding far-from-equilibrium processes, nonadiabatic (NA) molecular dynamics (MD) requires expensive calculations of the excitation energies and NA couplings. Machine learning (ML) can simplify computation; however, the NA Hamiltonian requires complex ML models due to its intricate relationship to atomic geometry. Working directly in the time domain, we employ bidirectional long short-term memory networks (Bi-LSTM) to interpolate the Hamiltonian. Applying this multiscale approach to three metal-halide perovskite systems, we achieve two orders of magnitude computational savings compared to direct ab initio calculation. Reasonable charge trapping and recombination times are obtained with NA Hamiltonian sampling every half a picosecond. The Bi-LSTM-NAMD method outperforms earlier models and captures both slow and fast time scales. In combination with ML force fields, the methodology extends NAMD simulation times from picoseconds to nanoseconds, comparable to charge carrier lifetimes in many materials. Nanosecond sampling is particularly important in systems containing defects, boundaries, interfaces, etc. that can undergo slow rearrangements.

Efficient Modeling of Quantum Dynamics of Charge Carriers in Materials Using Short Nonequilibrium Molecular Dynamics.

Nonadiabatic molecular dynamics provides essential insights into excited-state processes, but it is computationally intense and simplifications are needed. The classical path approximation provides critical savings. Still, long heating and equilibration steps are required. We demonstrate that practical results can be obtained with short, partially equilibrated ab initio trajectories. Once the system's structure is adequate and essential fluctuations are sampled, the nonadiabatic Hamiltonian can be constructed. Local structures require only 1-2 ps trajectories, as demonstrated with point defects in metal halide perovskites. Short trajectories represent anharmonic motions common in defective structures, an essential improvement over the harmonic approximation around the optimized geometry. Glassy systems, such as grain boundaries, require different simulation protocols, e.g., involving machine learning force fields. 10-fold shorter trajectories generate 10-20% time scale errors, which are acceptable, given experimental uncertainties and other approximations. The practical NAMD protocol enables fast screening of excited-state dynamics for rapid exploration of new materials.

Halide Vacancies Create No Charge Traps on Lead Halide Perovskite Surfaces but Can Generate Deep Traps in the Bulk.

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.

Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Inverse Fast Fourier Transform.

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.

Modification of Chloroplast Antioxidant Capacity by Plastid Transformation.

As immobile organisms, green plants must be frequently challenged by a broad range of environmental stresses. During these constantly adverse conditions, reactive oxygen species (ROS) levels can rise extremely in plants, leading to cellular dysfunction and cell death presumably due to irreversible protein overoxidation. Once considered merely as deleterious molecules, cells seek to remove them as efficiently as possible. To enhance ROS scavenging capacity, genes encoding antioxidative enzymes can be directly expressed from the genome of plastid (chloroplast), a major compartment for ROS production in photosynthetic organisms. Thus, overexpression of antioxidant enzymes by plastid engineering may provide an alternative to enhance plant's tolerance to stressful conditions specifically related with chloroplast-derived ROS. Here, we describe basic procedures for expressing glutathione reductase, a vital component of ascorbate-glutathione pathway, in tobacco via plastid transformation technology.

Electron-Volt Fluctuation of Defect Levels in Metal Halide Perovskites on a 100 ps Time Scale.

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 CH3NH3PbI3 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 TiO2, may simultaneously exhibit excellent performance and stability.

Fluctuations at Metal Halide Perovskite Grain Boundaries Create Transient Trap States: Machine Learning Assisted Ab Initio Analysis.

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) CsPbBr3 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.

Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Artificial Neural Networks.

Nonadiabatic (NA) molecular dynamics (MD) allows one to study far-from-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.

Significance of the Chemical Environment of an Element in Nonadiabatic Molecular Dynamics: Feature Selection and Dimensionality Reduction with Machine Learning.

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 CsPbI3, 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.

Review: The role of NADP-malic enzyme in plants under stress.

Under natural conditions, plants constantly encounter various fluctuating environmental stresses, which potentially restrict plant growth, plant development and even limit crop productivity. In addition to carbon fixation activity in C4 photosynthesis, NADP-dependent malic enzyme (NADP-ME) has been suggested to play important roles in diverse stress responses in plants. NADP-ME is one of the essential enzymes metabolizing malate, which is important for stabilizing cytoplasmic pH, controlling stomatal aperture, increasing resistance to aluminum excess and pathogen. Pyruvate, another product of NADP-ME reaction, participates in the synthesis of defense compounds such as flavonoids and lignin, which are involved in stresses tolerance such as mechanical wounding and pathogen invasion. Moreover, NADP-ME provides essential reductive coenzyme NADPH in the biosynthesis of flavonoids and lignin. On the other hand, NADPH is crucial for reactive active species (ROS) metabolizing systems such as the ascorbate-glutathione pathway and NADPH-dependent thioredoxin reductase, and is also required by apoplastic oxidative burst in most plant-pathogen interactions. This mini-review is largely focus on the characteristics of gene expression and activity of NADP-ME, as well as its interaction with ROS signaling under a variety of biotic and abiotic stress responses, which will provide a theoretical foundation for breeding of stress resistant crops.

Enhanced Tolerance to Methyl Viologen-Mediated Oxidative Stress via AtGR2 Expression From Chloroplast Genome.

Owing to their sessile life habit, plants are continuously subjected to a broad range of environmental stresses. During periods of (a)biotic stresses, reactive oxygen species (ROS) levels can rise excessively, leading to oxidative stress. Glutathione reductase (GR) plays an important role in scavenging the ROS and maintenance of redox potential of the cell during oxidative stress. To enhance ROS scavenging capacity, and hence stress tolerance, the Arabidopsis thalianaGR2 (AtGR2) gene was expressed from the tobacco plastid (chloroplast) genome, the main source of ROS production in plant photosynthetic tissues, in this study. Leaves of transplastomic tobacco plants had about seven times GR activity and 1.5 times total glutathione levels compared to wild type. These transplastomic tobacco plants showed no discernible phenotype and exhibited more tolerance to methyl viologen-induced oxidative stress than wild-type control plants. The results indicate that introducing AtGR2 in chloroplasts is an efficient approach to increase stress tolerance. This study also provides evidence that increasing antioxidant enzyme via plastid genome engineering is an alternative to enhance plant's tolerance to stressful conditions.