Mapping the phase-separated state in a 2D magnet.

Intrinsic 2D magnets have recently been established as a playground for studies on fundamentals of magnetism, quantum phases, and spintronic applications. The inherent instability at low dimensionality often results in coexistence and/or competition of different magnetic orders. Such instability of magnetic ordering may manifest itself as phase-separated states. In 4f 2D materials, magnetic phase separation is expressed in various experiments; however, the experimental evidence is circumstantial. Here, we employ a high-sensitivity MFM technique to probe the spatial distribution of magnetic states in the paradigmatic 4f 2D ferromagnet EuGe2. Below the ferromagnetic transition temperature, we discover the phase-separated state and follow its evolution with temperature and magnetic field. The characteristic length-scale of magnetic domains amounts to hundreds of nanometers. These observations strongly shape our understanding of the magnetic states in 2D materials at the monolayer limit and contribute to engineering of ultra-compact spintronics.

Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins.

Hydrogen atom transfer (HAT) reactions are important in many biological systems. As these reactions are hard to observe experimentally, it is of high interest to shed light on them using simulations. Here, we present a machine learning model based on graph neural networks for the prediction of energy barriers of HAT reactions in proteins. As input, the model uses exclusively non-optimized structures as obtained from classical simulations. It was trained on more than 17 000 energy barriers calculated using hybrid density functional theory. We built and evaluated the model in the context of HAT in collagen, but we show that the same workflow can easily be applied to HAT reactions in other biological or synthetic polymers. We obtain for relevant reactions (small reaction distances) a model with good predictive power (R2 ∼ 0.9 and mean absolute error of <3 kcal mol-1). As the inference speed is high, this model enables evaluations of dozens of chemical situations within seconds. When combined with molecular dynamics in a kinetic Monte-Carlo scheme, the model paves the way toward reactive simulations.

Shallow Silicon Vacancy Centers with Lifetime-Limited Optical Linewidths in Diamond Nanostructures.

The negatively charged silicon vacancy center (SiV-) in diamond is a promising, yet underexplored candidate for single-spin quantum sensing at sub-kelvin temperatures and tesla-range magnetic fields. A key ingredient for such applications is the ability to perform all-optical, coherent addressing of the electronic spin of near-surface SiV- centers. We present a robust and scalable approach for creating individual, ∼50 nm deep SiV- with lifetime-limited optical linewidths in diamond nanopillars through an easy-to-realize and persistent optical charge-stabilization scheme. The latter is based on single, prolonged 445 nm laser illumination that enables continuous photoluminescence excitation spectroscopy without the need for any further charge stabilization or repumping. Our results constitute a key step toward the use of near-surface, optically coherent SiV- for sensing under extreme conditions, and offer a powerful approach for stabilizing the charge-environment of diamond color centers for quantum technology applications.

Accurate GW frontier orbital energies of 134 kilo molecules.

HOMO and LUMO energies are critical molecular properties that typically require high accuracy computations for practical applicability. Until now, a comprehensive dataset containing sufficiently accurate HOMO and LUMO energies has been unavailable. In this study, we introduce a new dataset of HOMO/LUMO energies for QM9 compounds, calculated using the GW method. The GW method offers adequate HOMO/LUMO prediction accuracy for diverse applications, exhibiting mean unsigned errors of 100 meV in the GW100 benchmark dataset. This database may serve as a benchmark of HOMO/LUMO prediction, delta-learning, and transfer learning, particularly for larger molecules where GW is the most accurate but still numerically feasible method. We anticipate that this dataset will enable the development of more accurate machine learning models for predicting molecular properties.

Temperature-Dependent Photophysics of Single NV Centers in Diamond.

We present a comprehensive study of the temperature- and magnetic-field-dependent photoluminescence (PL) of individual NV centers in diamond, spanning the temperature-range from cryogenic to ambient conditions. We directly observe the emergence of the NV's room-temperature effective excited-state structure and provide a clear explanation for a previously poorly understood broad quenching of NV PL at intermediate temperatures around 50 K, as well as the subsequent revival of NV PL. We develop a model based on two-phonon orbital averaging that quantitatively explains all of our findings, including the strong impact that strain has on the temperature dependence of the NV's PL. These results complete our understanding of orbital averaging in the NV excited state and have significant implications for the fundamental understanding of the NV center and its applications in quantum sensing.

Proximity-induced chiral quantum light generation in strain-engineered WSe2/NiPS3 heterostructures.

Quantum light emitters capable of generating single photons with circular polarization and non-classical statistics could enable non-reciprocal single-photon devices and deterministic spin-photon interfaces for quantum networks. To date, the emission of such chiral quantum light relies on the application of intense external magnetic fields, electrical/optical injection of spin-polarized carriers/excitons or coupling with complex photonic metastructures. Here we report the creation of free-space chiral quantum light emitters via the nanoindentation of monolayer WSe2/NiPS3 heterostructures at zero external magnetic field. These quantum light emitters emit with a high degree of circular polarization (0.89) and single-photon purity (95%), independent of pump laser polarization. Scanning diamond nitrogen-vacancy microscopy and temperature-dependent magneto-photoluminescence studies reveal that the chiral quantum light emission arises from magnetic proximity interactions between localized excitons in the WSe2 monolayer and the out-of-plane magnetization of defects in the antiferromagnetic order of NiPS3, both of which are co-localized by strain fields associated with the nanoscale indentations.

Impact of the first COVID-19 lockdown in Austria on laboratory and mental parameters in diabetic patients: a retrospective analysis highlighting the importance of regular medical consultations in a patient cohort.

BACKGROUND: Due to the COVID-19 pandemic, the first lockdown was implemented in Austria for almost 7 weeks. In contrast to many other countries, medical consultations were permitted, either by telemedicine or at doctors' offices. Nevertheless, restrictions related to this lockdown could possibly cause an increased risk of deterioration in health, especially in diabetes. This study aimed to assess the impact of Austria's first lockdown on laboratory and mental parameters in a type-2 diabetes mellitus cohort. METHODS: Overall 347 mainly elderly patients with type-2 diabetes (56% male; aged 63.7±10.1 years) were included in this retrospective practitioner-based analysis. Laboratory as well as mental parameters were compared from before and after the lockdown. RESULTS: The lockdown showed no significant effect on HbA1c levels. On the other hand, total cholesterol (P<0.001) and LDL cholesterol (P<0.001) levels improved significantly, whereas body weight (P<0.01) and mental well-being based on the EQ-5D-3L questionnaire (P<0.01) increased significantly in terms of worsening. CONCLUSIONS: Lack of movement and staying at home resulted in a significant weight gain and worsening of mental well-being in type-2 diabetes during the first lockdown in Austria. Thanks to regular medical consultations, laboratory parameters remained stable or even improved. Thus, routine health check-ups in mainly elderly type 2 diabetic patients are essential to minimize the deterioration of health conditions during lockdowns.

Integrated System Built for Small-Molecule Semiconductors via High-Throughput Approaches.

High-throughput synthesis of solution-processable structurally variable small-molecule semiconductors is both an opportunity and a challenge. A large number of diverse molecules provide a possibility for quick material discovery and machine learning based on experimental data. However, the diversity of the molecular structure leads to the complexity of molecular properties, such as solubility, polarity, and crystallinity, which poses great challenges to solution processing and purification. Here, we first report an integrated system for the high-throughput synthesis, purification, and characterization of molecules with a large variety. Based on the principle "Like dissolves like," we combine theoretical calculations and a robotic platform to accelerate the purification of those molecules. With this platform, a material library containing 125 molecules and their optical-electronic properties was built within a timeframe of weeks. More importantly, the high repeatability of recrystallization we design is a reliable approach to further upgrading and industrial production.

Modeling Charge Transport in Organic Semiconductors Using Neural Network Based Hamiltonians and Forces.

The fewest switches surface hopping method has been widely used for the simulation of charge transport in organic semiconductors. In the present study, we perform nonadiabatic molecular dynamics (NAMD) simulations of hole transport in anthracene and pentacene. The simulations employ neural network (NN) based Hamiltonians in two different nuclear relaxation schemes, which utilize either a precalculated reorganization energy or site energy gradients additionally obtained from NN models. The performance of the NN models is evaluated in reproducing hole mobilities and inverse participation ratios in terms of both quality and computational cost. The results show that charge mobilities and inverse participation ratios obtained by models, which were trained on DFTB or DFT training data, are in very good agreement with the respective QM reference method for implicit relaxation and, where available, also for explicit relaxation. Reasonable agreement with experimental hole mobilities is achieved. Utilizing our models in NAMD simulations of charge transfer amounts to a reduction of the computational cost in a range of 1 to 7 orders of magnitude compared to DFTB and DFT. This proves neural networks as promising tools for the improvement of accuracy and efficiency of charge and potentially also exciton transport simulations in complex and large molecular systems.

High-Throughput Synthesis and Machine Learning Assisted Design of Photodegradable Hydrogels.

Due to the large chemical space, the design of functional and responsive soft materials poses many challenges but also offers a wide range of opportunities in terms of the scope of possible properties. Herein, an experimental workflow for miniaturized combinatorial high-throughput screening of functional hydrogel libraries is reported. The data created from the analysis of the photodegradation process of more than 900 different types of hydrogel pads are used to train a machine learning model for automated decision making. Through iterative model optimization based on Bayesian optimization, a substantial improvement in response properties is achieved and thus expanded the scope of material properties obtainable within the chemical space of hydrogels in the study. It is therefore demonstrated that the potential of combining miniaturized high-throughput experiments with smart optimization algorithms for cost and time efficient optimization of materials properties.

Graph neural networks for materials science and chemistry.

Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.

Low-Temperature Photophysics of Single Nitrogen-Vacancy Centers in Diamond.

We investigate the magnetic field dependent photophysics of individual nitrogen-vacancy (NV) color centers in diamond under cryogenic conditions. At distinct magnetic fields, we observe significant reductions in the NV photoluminescence rate, which indicate a marked decrease in the optical readout efficiency of the NV's ground state spin. We assign these dips to excited state level anticrossings, which occur at magnetic fields that strongly depend on the effective, local strain environment of the NV center. Our results offer new insights into the structure of the NVs' excited states and a new tool for their effective characterization. Using this tool, we observe strong indications for strain-dependent variations of the NV's orbital g factor, obtain new insights into NV charge state dynamics, and draw important conclusions regarding the applicability of NV centers for low-temperature quantum sensing.

Automatic discovery of photoisomerization mechanisms with nanosecond machine learning photodynamics simulations.

Photochemical reactions are widely used by academic and industrial researchers to construct complex molecular architectures via mechanisms that often require harsh reaction conditions. Photodynamics simulations provide time-resolved snapshots of molecular excited-state structures required to understand and predict reactivities and chemoselectivities. Molecular excited-states are often nearly degenerate and require computationally intensive multiconfigurational quantum mechanical methods, especially at conical intersections. Non-adiabatic molecular dynamics require thousands of these computations per trajectory, which limits simulations to ∼1 picosecond for most organic photochemical reactions. Westermayr et al. recently introduced a neural-network-based method to accelerate the predictions of electronic properties and pushed the simulation limit to 1 ns for the model system, methylenimmonium cation (CH2NH2 +). We have adapted this methodology to develop the Python-based, Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI2MD) software for the cis-trans isomerization of trans-hexafluoro-2-butene and the 4π-electrocyclic ring-closing of a norbornyl hexacyclodiene. We performed a 10 ns simulation for trans-hexafluoro-2-butene in just 2 days. The same simulation would take approximately 58 years with traditional multiconfigurational photodynamics simulations. We generated training data by combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to adaptively sample sparse data regions along reaction coordinates. The final data set of the cis-trans isomerization and the 4π-electrocyclic ring-closing model has 6207 and 6267 data points, respectively. The training errors in energy using feedforward neural networks achieved chemical accuracy (0.023-0.032 eV). The neural network photodynamics simulations of trans-hexafluoro-2-butene agree with the quantum chemical calculations showing the formation of the cis-product and reactive carbene intermediate. The neural network trajectories of the norbornyl cyclohexadiene corroborate the low-yielding syn-product, which was absent in the quantum chemical trajectories, and revealed subsequent thermal reactions in 1 ns.

Analyzing Dynamical Disorder for Charge Transport in Organic Semiconductors via Machine Learning.

Organic semiconductors are indispensable for today's display technologies in the form of organic light-emitting diodes (OLEDs) and further optoelectronic applications. However, organic materials do not reach the same charge carrier mobility as inorganic semiconductors, limiting the efficiency of devices. To find or even design new organic semiconductors with higher charge carrier mobility, computational approaches, in particular multiscale models, are becoming increasingly important. However, such models are computationally very costly, especially when large systems and long timescales are required, which is the case to compute static and dynamic energy disorder, i.e., the dominant factor to determine charge transport. Here, we overcome this drawback by integrating machine learning models into multiscale simulations. This allows us to obtain unprecedented insight into relevant microscopic materials properties, in particular static and dynamic disorder contributions for a series of application-relevant molecules. We find that static disorder and thus the distribution of shallow traps are highly asymmetrical for many materials, impacting widely considered Gaussian disorder models. We furthermore analyze characteristic energy level fluctuation times and compare them to typical hopping rates to evaluate the importance of dynamic disorder for charge transport. We hope that our findings will significantly improve the accuracy of computational methods used to predict application-relevant materials properties of organic semiconductors and thus make these methods applicable for virtual materials design.

Time-Resolved sub-Ångström Metrology by Temporal Phase Interferometry near X-Ray Resonances of Nuclei.

We introduce an analytical phase-reconstruction principle that retrieves atomic scale motion via time-domain interferometry. The approach is based on a resonant interaction with high-frequency light and does not require temporal resolution on the time scale of the resonance period. It is thus applicable to hard x rays and γ rays for measurements of extremely small spatial displacements or relative-frequency changes. Here, it is applied to retrieve the temporal phase of a 14.4 keV emission line of an ^{57}Fe sample, which corresponds to a spatial translation of this sample. The small wavelength of this transition (λ=0.86 Å) allows for determining the motion of the emitter on sub-Ångström length and nanosecond timescales.

Electric-Field-Controlled Dopant Distribution in Organic Semiconductors.

Stable electrical doping of organic semiconductors is fundamental for the functionality of high performance devices. It is known that dopants can be subjected to strong diffusion in certain organic semiconductors. This work studies the impact of operating conditions on thin films of the polymer poly(3-hexylthiophene) (P3HT) and the small molecule Spiro-MeOTAD, doped with two differently sized p-type dopants. The negatively charged dopants can drift upon application of an electric field in thin films of doped P3HT over surprisingly large distances. This drift is not observed in the small molecule Spiro-MeOTAD. Upon the dopants' directional movement in P3HT, a dedoped region forms at the negatively biased electrode, increasing the overall resistance of the thin film. In addition to electrical measurements, optical microscopy, spatially resolved infrared spectroscopy, and scanning Kelvin probe microscopy are used to investigate the drift of dopants. Dopant mobilities of 10-9 to 10-8 cm2 V-1 s-1 are estimated. This drift over several micrometers is reversible and can be controlled. Furthermore, this study presents a novel memory device to illustrate the applicability of this effect. The results emphasize the importance of dynamic processes under operating conditions that must be considered even for single doped layers.