Application of Machine Learning Algorithms to Metadynamics for the Elucidation of the Binding Modes and Free Energy Landscape of Drug/Target Interactions: a Case Study.

In the context of drug discovery, computational methods were able to accelerate the challenging process of designing and optimizing a new drug candidate. Amongst the possible atomistic simulation approaches, metadynamics (metaD) has proven very powerful. However, the choice of collective variables (CVs) is not trivial for complex systems. To automate the process of CVs identification, two different machine learning algorithms were applied in this study, namely DeepLDA and Autoencoder, to the metaD simulation of a well-researched drug/target complex, consisting in a pharmacologically relevant non-canonical DNA secondary structure (G-quadruplex) and a metallodrug acting as its stabilizer, as well as solvent molecules.

Machine learning and DFT investigation of CO, CO2 and CH4 adsorption on pristine and defective two-dimensional magnesene.

Adsorption study of environmentally toxic small gas molecules on two-dimensional (2D) materials plays a significant role in analyzing the performance of sensors. In this work, density functional theory (DFT) and machine learning (ML) techniques have been employed to systematically study the adsorption properties of CO, CO2, and CH4 gas molecules on the pristine and defective planar magnesium monolayer, known as magnesene (2D-Mg). The DFT analysis showed that mechanically robust 2D-Mg retains its metallicity in the presence of both mono and di-vacancy defects. Our observations have shown that 2D-Mg, whether defective or pristine, exhibits distinct adsorption behaviors towards CO, CO2, and CH4 gas molecules, including varying chemisorption and physisorption, charge transfer, and distance from the gas molecules. When analyzing the recovery time of gas molecules at room temperature, it is clear that adsorption energy has a direct correlation with the adsorption-desorption cycles, and CH4 possesses an ultra-low recovery time (15.27 ps) compared to CO2 (1.04 ns) and CO (0.90 µs) molecules. The analysis showed that defects do not have a significant impact on the work function of 2D-Mg. However, the work function decreased upon adsorption of CH4, resulting in improved sensitivity due to changes in the electronic properties. Additionally, we explored supervised ML regression models to evaluate their ability to act as a surrogate for the DFT-based adsorption energy calculation. Using both system statistics and smooth overlap of atomic position (SOAP)-based featurization, we observed that adsorption energies can be predicted with a mean absolute error of 0.10 eV.

Utilizing Data-Driven Optimization to Automate the Parametrization of Kinetic Monte Carlo Models.

Kinetic Monte Carlo (kMC) simulations are a popular tool to investigate the dynamic behavior of stochastic systems. However, one major limitation is their relatively high computational costs. In the last three decades, significant effort has been put into developing methodologies to make kMC more efficient, resulting in an enhanced runtime efficiency. Nevertheless, kMC models remain computationally expensive. This is in particular an issue in complex systems with several unknown input parameters where often most of the simulation time is required for finding a suitable parametrization. A potential route for automating the parametrization of kinetic Monte Carlo models arises from coupling kMC with a data-driven approach. In this work, we equip kinetic Monte Carlo simulations with a feedback loop consisting of Gaussian Processes (GPs) and Bayesian optimization (BO) to enable a systematic and data-efficient input parametrization. We utilize the results from fast-converging kMC simulations to construct a database for training a cheap-to-evaluate surrogate model based on Gaussian processes. Combining the surrogate model with a system-specific acquisition function enables us to apply Bayesian optimization for the guided prediction of suitable input parameters. Thus, the amount of trial simulation runs can be considerably reduced facilitating an efficient utilization of arbitrary kMC models. We showcase the effectiveness of our methodology for a physical process of growing industrial relevance: the space-charge layer formation in solid-state electrolytes as it occurs in all-solid-state batteries. Our data-driven approach requires only 1-2 iterations to reconstruct the input parameters from different baseline simulations within the training data set. Moreover, we show that the methodology is even capable of accurately extrapolating into regions outside the training data set which are computationally expensive for direct kMC simulation. Concluding, we demonstrate the high accuracy of the underlying surrogate model via a full parameter space investigation eventually making the original kMC simulation obsolete.

Rational design of selenium inserted 1T/2H mixed-phase molybdenum disulfide for energy storage and pollutant degradation applications.

MoS2based materials are recognized as the promising candidate for multifunctional applications due to its unique physicochemical properties. But presence of lower number of active sites, poor electrical conductivity, and less stability of 2H and 1T MoS2inherits its practical applications. Herein, we synthesized the Se inserted mixed-phase 2H/1T MoS2nanosheets with abundant defects sites to achieve improved overall electrochemical activity. Moreover, the chalcogen insertion induces the recombination of photogenerated excitons and enhances the life of carriers. The bifunctional energy storage and photocatalytic pollutant degradation studies of the prepare materials are carried out. Fabricated symmetric solid-state supercapacitor showed an exceptional capacitance of 178 mF cm-2with an excellent energy density of 8µWh cm-2and power density of 137 mW cm-2, with remarkable capacitance retention of 86.34% after successive 8000 charge-discharge cycles. The photocatalytic dye degradation experiments demonstrate that the prepared Se incorporated 1T/2H MoS2is a promising candidate for dye degradation applications. Further, the DFT studies confirmed that the Se inserted MoS2is a promising electrode material for supercapacitor applications with higherCQdue to a larger density of states near Fermi level as compared to pristine MoS2.

Advanced MRI imaging of nerve roots in lumbar radiculopathy due to discoradicular conflict: DWI, DTI, and T2 mapping with clinical and neurophysiological correlations.

PURPOSE: To evaluate the lumbar nerve root alterations in patients with lumbar disc herniation sciatica using advanced multimodality MRI sequences and the correlations with clinical and neurophysiological findings. MATERIAL AND METHODS: We prospectively evaluated 45 patients suffering from unilateral lumbar radiculopathy due to disco radicular conflict. All patients underwent MRI examinations using a standard MRI protocol and additional advanced MRI sequences (DWI, DTI, and T2 mapping sequences). Relative metrics of ADC, FA, and T2 relaxation times were recorded by placing ROIs at the pre-, foraminal, and post-foraminal level, either at the affected side or the contralateral side, used as control. All patients were also submitted to electromyography testing, recording the spontaneous activity, voluntary activity, F wave amplitude, latency, and motor evoked potentials (MEP) amplitude and latency, both at the level of the tibialis anterior and the gastrocnemius. Clinical features (diseases duration, pain, sensitivity, strength, osteotendinous reflexes) were also recorded. RESULTS: Among clinical features, we found a positive correlation of pain intensity with ADC values of the lumbar nerve roots. The presence of spontaneous activity was correlated with lower ADC values of the affected lumbar nerve root. F wave and MEP latency were correlated with decreased FA values at the foraminal level and increased values at the post-foraminal level. The same neurophysiological measures correlated positively with pre-foraminal T2 mapping values and negatively with post-foraminal T2 mapping values. Increased T2 mapping values at the foraminal level were correlated with disease duration. CONCLUSIONS: Evaluation of lumbar nerve roots using advanced MRI sequences may provide useful clinical information in patients with lumbar radiculopathy, potentially indicating active inflammation/myelinic damage (DTI, T2 mapping) and axonal damage/chronicity (DWI).

An Instrument for the Characterization and Calibration of Optical Sensors.

This paper presents the development of a hardware/software system for the characterization of the electronic response of optical (camera) sensors such as matrix and linear color and monochrome Charge Coupled Device (CCD) or Complementary Metal Oxide Semiconductor (CMOS). The electronic response of a sensor is required for inspection purposes. It also allows the design and calibration of the integrating device to achieve the desired performance. The proposed instrument equipment fulfills the most recent European Machine Vision Association (EMVA) 1288 standard ver. 3.1: the spatial non uniformity of the illumination ΔE must be under 3%, and the sensor must achieve an f-number of 8.0 concerning the light source. The following main innovations have achieved this: an Ulbricht sphere providing a uniform light distribution (irradiation) of 99.54%; an innovative illuminator with proper positioning of color Light Emitting Diodes (LEDs) and control electronics; and a flexible C# program to analyze the sensor parameters, namely Quantum Efficiency, Overall System Gain, Temporal Dark Noise, Dark Signal Non Uniformity (DSNU1288), Photo Response Non-Uniformity (PRNU1288), Maximum achievable Signal to Noise Ratio (SNRmax), Absolute sensitivity threshold, Saturation Capacity, Dynamic Range, and Dark Current. This new instrument has allowed a camera manufacturer to design, integrate, and inspect numerous devices and camera models (Necta, Celera, and Aria).

Deep Learning Total Energies and Orbital Energies of Large Organic Molecules Using Hybridization of Molecular Fingerprints.

The ability to predict material properties without the need for resource-consuming experimental efforts can immensely accelerate material and drug discovery. Although ab initio methods can be reliable and accurate in making such predictions, they are computationally too expensive on a large scale. The recent advancements in artificial intelligence and machine learning as well as the availability of large quantum mechanics derived datasets enable us to train models on these datasets as a benchmark and to make fast predictions on much larger datasets. The success of these machine learning models highly depends on the machine-readable fingerprints of the molecules that capture their chemical properties as well as topological information. In this work, we propose a common deep learning-based framework to combine different types of molecular fingerprints to enhance prediction accuracy. A graph neural network (GNN), many-body tensor representation (MBTR), and a set of simple molecular descriptors (MD) were used to predict the total energies, highest occupied molecular orbital (HOMO) energies, and lowest unoccupied molecular orbital (LUMO) energies of a dataset containing ∼62k large organic molecules with complex aromatic rings and remarkably diverse functional groups. The results demonstrate that a combination of best performing molecular fingerprints can produce better results than the individual ones. The simple and flexible deep learning framework developed in this work can be easily adapted to incorporate other types of molecular fingerprints.

Acceleration scheme for particle transport in kinetic Monte Carlo methods.

Kinetic Monte Carlo (kMC) simulations are frequently used to study (electro-)chemical processes within science and engineering. kMC methods provide insight into the interplay of stochastic processes and can link atomistic material properties with macroscopic characteristics. Significant problems concerning the computational demand arise if processes with large time disparities are competing. Acceleration algorithms are required to make slow processes accessible. Especially, the accelerated superbasin kMC (AS-kMC) scheme has been frequently applied within chemical reaction networks. For larger systems, the computational overhead of the AS-kMC is significant as the computation of the superbasins is done during runtime and comes with the need for large databases. Here, we propose a novel acceleration scheme for diffusion and transport processes within kMC simulations. Critical superbasins are detected during the system initialization. Scaling factors for the critical rates within the superbasins, as well as a lower bound for the number of sightings, are derived. Our algorithm exceeds the AS-kMC in the required simulation time, which we demonstrate with a 1D-chain example. In addition, we apply the acceleration scheme to study the time-of-flight (TOF) of charge carriers within organic semiconductors. In this material class, time disparities arise due to a significant spread of transition rates. The acceleration scheme allows a significant acceleration up to a factor of 65 while keeping the error of the TOF values negligible. The computational overhead is negligible, as all superbasins only need to be computed once.

Stepping Out of Equilibrium: The Quest for Understanding the Role of Non-Equilibrium (Thermo-)Dynamics in Electronic and Electrochemical Processes.

This editorial aims to interest researchers and inspire novel research on the topic of non-equilibrium Thermodynamics and Monte Carlo for Electronic and Electrochemical Processes. We present a brief outline on recent progress and challenges in the study of non-equilibrium dynamics and thermodynamics using numerical Monte Carlo simulations. The aim of this special issue is to collect recent advances and novel techniques of Monte Carlo methods to study non-equilibrium electronic and electrochemical processes at the nanoscale.

Correction: Influence of permittivity and energetic disorder on the spatial charge carrier distribution and recombination in organic bulk-heterojunctions.

Correction for 'Influence of permittivity and energetic disorder on the spatial charge carrier distribution and recombination in organic bulk-heterojunctions' by Tim Albes et al., Phys. Chem. Chem. Phys., 2017, 19, 20974-20983.

Optimizing the Size of Platinum Nanoparticles for Enhanced Mass Activity in the Electrochemical Oxygen Reduction Reaction.

High oxygen reduction (ORR) activity has been for many years considered as the key to many energy applications. Herein, by combining theory and experiment we prepare Pt nanoparticles with optimal size for the efficient ORR in proton-exchange-membrane fuel cells. Optimal nanoparticle sizes are predicted near 1, 2, and 3 nm by computational screening. To corroborate our computational results, we have addressed the challenge of approximately 1 nm sized Pt nanoparticle synthesis with a metal-organic framework (MOF) template approach. The electrocatalyst was characterized by HR-TEM, XPS, and its ORR activity was measured using a rotating disk electrode setup. The observed mass activities (0.87±0.14 A mgPt -1 ) are close to the computational prediction (0.99 A mgPt -1 ). We report the highest to date mass activity among pure Pt catalysts for the ORR within similar size range. The specific and mass activities are twice as high as the Tanaka commercial Pt/C catalysis.

Charge carrier mobility of disordered organic semiconductors with correlated energetic and spatial disorder.

Low charge carrier mobility is one key factor limiting the performance and applicability of devices based on organic semiconductors. Theoretical studies on mobility using the kinetic Monte Carlo or master equation are mainly based on a Gaussian energetic disorder and regular cubic lattices. The dependence of mobility on the electric field, temperature and charge carrier density is well studied for the Gaussian disorder model. In this work, we investigate the influence of spatially correlated site energies and spatial disorder in the lattice sites on the mobility using kinetic Monte Carlo simulations. Our analysis is based on both a regular cubic and a non-cubic Voronoi lattice. The latter is used to include spatial disorder in order to study its influence on the mobility for amorphous organic materials. Our results show that charge carrier mobility is strongly influenced by correlations in the site energies. Strong correlations even invert the field dependence of the mobility as observed experimentally in semi-crystalline polymers such as P3HT. Evaluation of local currents between localized states reveals the formation of current filaments with increasing correlation. Furthermore, the influence of the electric field and the energy landscape on the transport energy is studied by evaluation of active sites. A strong correlation between the transport energy, filaments in the local currents and the charge carrier mobility is observed. Our studies on the spatial disorder model do not indicate an inversion of the field dependence as observed by other researchers. The negative field-dependence in semi-crystalline materials may be explained by a higher correlation in the site energies as shown in a strongly correlated energetic landscape.

Influence of permittivity and energetic disorder on the spatial charge carrier distribution and recombination in organic bulk-heterojunctions.

In bulk-heterojunction organic solar cells the low permittivity in combination with the spatial and energetic disorder of the organic materials lead to a complex behavior of charge carriers within the active layer. Charges originate from exciton splitting at the heterojunction interface and the successive interplay between mutual Coulomb interactions and transport through the disordered organic can lead to insufficient separation from the interface, increased interface densities with respect to the bulk regions and, hence, affect recombination. To further understand the mechanisms of recombination, insight into the explicit spatial distribution of charge carriers within the blend is crucial. We performed kinetic Monte Carlo simulations on a bulk-heterojunction organic solar cell to assess the effect of Coulomb interactions and energetic disorder on the three-dimensional spatial distribution of charge carriers and highlight the correlation with both geminate and non-geminate recombination. We show that for materials with low permittivity and large energetic disorder the charge distribution is strongly inhomogeneous with accumulation along the heterojunction interface. In such cases recombination is not limited by recombination partners finding each other but rather an interface controlled process where geminate recombination dominates over nongeminate recombination.

Single-molecule electronics: Cooling individual vibrational modes by the tunneling current.

Electronic devices composed of single molecules constitute the ultimate limit in the continued downscaling of electronic components. A key challenge for single-molecule electronics is to control the temperature of these junctions. Controlling heating and cooling effects in individual vibrational modes can, in principle, be utilized to increase stability of single-molecule junctions under bias, to pump energy into particular vibrational modes to perform current-induced reactions, or to increase the resolution in inelastic electron tunneling spectroscopy by controlling the life-times of phonons in a molecule by suppressing absorption and external dissipation processes. Under bias the current and the molecule exchange energy, which typically results in heating of the molecule. However, the opposite process is also possible, where energy is extracted from the molecule by the tunneling current. Designing a molecular "heat sink" where a particular vibrational mode funnels heat out of the molecule and into the leads would be very desirable. It is even possible to imagine how the vibrational energy of the other vibrational modes could be funneled into the "cooling mode," given the right molecular design. Previous efforts to understand heating and cooling mechanisms in single molecule junctions have primarily been concerned with small models, where it is unclear which molecular systems they correspond to. In this paper, our focus is on suppressing heating and obtaining current-induced cooling in certain vibrational modes. Strategies for cooling vibrational modes in single-molecule junctions are presented, together with atomistic calculations based on those strategies. Cooling and reduced heating are observed for two different cooling schemes in calculations of atomistic single-molecule junctions.

IETS and quantum interference: propensity rules in the presence of an interference feature.

Destructive quantum interference in single molecule electronics is an intriguing phenomenon; however, distinguishing quantum interference effects from generically low transmission is not trivial. In this paper, we discuss how quantum interference effects in the transmission lead to either low current or a particular line shape in current-voltage curves, depending on the position of the interference feature. Second, we consider how inelastic electron tunneling spectroscopy can be used to probe the presence of an interference feature by identifying vibrational modes that are selectively suppressed when quantum interference effects dominate. That is, we expand the understanding of propensity rules in inelastic electron tunneling spectroscopy to molecules with destructive quantum interference.

Possibility of a field effect transistor based on Dirac particles in semiconducting anatase-TiO2 nanowires.

In Dirac materials, like graphene or topological insulators, massless pseudorelativistic electrons promise new, very fast electronic devices by utilizing the partial suppression of backscattering. However, the semimetal nature of graphene makes the realization of practical field effect transistors difficult, due to small on-off current ratios. Here, we propose a new concept, based on Dirac states inside the conduction (or valence) band of a lightly doped wide band gap semiconductor. With the application of a gate voltage, the Dirac states become populated; that is, the Fermi level is switched between the "classical" high-resistivity semiconducting and the relativistic high-mobility metallic range. We demonstrate by theoretical calculations that such a transition can be realized, for example, in thin anatase nanowires, which have been synthesized before. Ta-doped anatase nanowires offer an excellent possibility to build field effect transistors with high speed and good on-off ratio. Guidelines for finding similar "Dirac semiconductors" are provided.

Artificial Intelligence-Based, Wavelet-Aided Prediction of Long-Term Outdoor Performance of Perovskite Solar Cells.

The commercial development of perovskite solar cells (PSCs) has been significantly delayed by the constraint of performing time-consuming degradation studies under real outdoor conditions. These are necessary steps to determine the device lifetime, an area where PSCs traditionally suffer. In this work, we demonstrate that the outdoor degradation behavior of PSCs can be predicted by employing accelerated indoor stability analyses. The prediction was possible using a swift and accurate pipeline of machine learning algorithms and mathematical decompositions. By training the algorithms with different indoor stability data sets, we can determine the most relevant stress factors, thereby shedding light on the outdoor degradation pathways. Our methodology is not specific to PSCs and can be extended to other PV technologies where degradation and its mechanisms are crucial elements of their widespread adoption.

Emerging indoor photovoltaics for self-powered and self-aware IoT towards sustainable energy management.

As the number of Internet of Things devices is rapidly increasing, there is an urgent need for sustainable and efficient energy sources and management practices in ambient environments. In response, we developed a high-efficiency ambient photovoltaic based on sustainable non-toxic materials and present a full implementation of a long short-term memory (LSTM) based energy management using on-device prediction on IoT sensors solely powered by ambient light harvesters. The power is supplied by dye-sensitised photovoltaic cells based on a copper(ii/i) electrolyte with an unprecedented power conversion efficiency at 38% and 1.0 V open-circuit voltage at 1000 lux (fluorescent lamp). The on-device LSTM predicts changing deployment environments and adapts the devices' computational load accordingly to perpetually operate the energy-harvesting circuit and avoid power losses or brownouts. Merging ambient light harvesting with artificial intelligence presents the possibility of developing fully autonomous, self-powered sensor devices that can be utilized across industries, health care, home environments, and smart cities.

Rapid Data-Efficient Optimization of Perovskite Nanocrystal Syntheses through Machine Learning Algorithm Fusion.

With the demand for renewable energy and efficient devices rapidly increasing, a need arises to find and optimize novel (nano)materials. With sheer limitless possibilities for material combinations and synthetic procedures, obtaining novel, highly functional materials has been a tedious trial and error process. Recently, machine learning has emerged as a powerful tool to help optimize syntheses; however, most approaches require a substantial amount of input data, limiting their pertinence. Here, three well-known machine-learning models are merged with Bayesian optimization into one to optimize the synthesis of CsPbBr3 nanoplatelets with limited data demand. The algorithm can accurately predict the photoluminescence emission maxima of nanoplatelet dispersions using only the three precursor ratios as input parameters. This allows us to fabricate previously unobtainable seven and eight monolayer-thick nanoplatelets. Moreover, the algorithm dramatically improves the homogeneity of 2-6-monolayer-thick nanoplatelet dispersions, as evidenced by narrower and more symmetric photoluminescence spectra. Decisively, only 200 total syntheses are required to achieve this vast improvement, highlighting how rapidly material properties can be optimized. The algorithm is highly versatile and can incorporate additional synthetic parameters. Accordingly, it is readily applicable to other less-explored nanocrystal syntheses and can help rapidly identify and improve exciting compositions' quality.

Mass transport and charge transfer through an electrified interface between metallic lithium and solid-state electrolytes.

All-solid-state Li-ion batteries are one of the most promising energy storage devices for future automotive applications as high energy density metallic Li anodes can be safely used. However, introducing solid-state electrolytes needs a better understanding of the forming electrified electrode/electrolyte interface to facilitate the charge and mass transport through it and design ever-high-performance batteries. This study investigates the interface between metallic lithium and solid-state electrolytes. Using spectroscopic ellipsometry, we detected the formation of the space charge depletion layers even in the presence of metallic Li. That is counterintuitive and has been a subject of intense debate in recent years. Using impedance measurements, we obtain key parameters characterizing these layers and, with the help of kinetic Monte Carlo simulations, construct a comprehensive model of the systems to gain insights into the mass transport and the underlying mechanisms of charge accumulation, which is crucial for developing high-performance solid-state batteries.