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The rapid development of e-learning has drawn increasing attention to the issue of how learners’ learning activities can be better structured using technologies. This study focuses on how to improve e-learning performance by optimizing the structuring of learning sessions from the perspective of interleaving (i.e., mixing different topics in a learning session). Following the design science paradigm, this study chooses cognitive load theory as the kernel theory and proposes a new interleaving design—related-interleaving —that populates an interleaved session with related topics as a way of reducing cognitive load during an interleaved session. Drawing on the theoretical predictions, we design and instantiate a personalized learning system with the related-interleaving strategy by fusing educational strategies and machine learning techniques. The results from a two-month field experiment confirm that related-interleaving outperforms non-interleaving and unrelated-interleaving. Our findings also reveal that compared with unrelated-interleaving, related-interleaving benefits weak learners more and thus helps reduce learning performance disparities. This study demonstrates how personalized e-learning systems can be further improved from the perspective of interleaving. [ABSTRACT FROM AUTHOR]

Predictive analytics is the approach to business analytics that answers the question of what might happen in the future. Although predictive information is critical for making forward-looking decisions, traditional approaches struggle to cope with the increasing uncertainty and complexity that characterise modern supply chains. Simulation is limited by insufficient timeliness, while machine learning is constrained by poor interpretability and data scarcity. Inspired by the complementary nature of simulation and machine learning, an integrated predictive analytics approach is proposed and applied to a humanitarian supply chain. By coupling simulation and machine learning, predictive models can be developed with limited historical data, and pre-crisis performance assessment can be performed to facilitate timely and informed decisions. The proposed approach enables managers to gain valuable insights into the complex evolution of the uncertain future, which also opens up the possibility of further integration with optimisation and digital twins. [ABSTRACT FROM AUTHOR]

Shop scheduling is deeply involved in manufacturing. In order to improve the efficiency of scheduling and fit dynamic scenarios, many Deep Reinforcement Learning (DRL) methods are studied to solve scheduling problems like job shop and flow shop. But most studies focus on using the latest algorithms while ignoring that the environment plays an important role in agent learning. In this paper, we design an effective, robust and size-agnostic environment for job shop scheduling. The proposed design of environment uses centralised training and decentralised execution (CTDE) to implement a multi-agent architecture. Together with the observation space we design, environmental information that is irrelevant to the current decision is eliminated as much as possible. The proposed action space enlarges the decision space of agents, which performs better than the traditional way. Finally, Soft Actor-Critic (SAC) algorithm is adapted to learning within this environment. By comparing with traditional scheduling rules, other reinforcement learning algorithms, and relevant literature, the superiority of the results obtained in this study is demonstrated. [ABSTRACT FROM AUTHOR]

Online reviews provide rich information on customer satisfaction, displaying various numeric ratings as well as detailed explanations presented in written form. However, analyzing such data is challenging due to the unstructured nature of text. This article introduces a novel machine-learning method for identifying interpretable key drivers of star ratings from text reviews, which might vary across segments. By adopting the Ising model prior to account for dependence between words, the model simultaneously achieves segmentation, identifies segment-level key topics (i.e., groups of frequently co-occurring words), and estimates the impacts of the selected words on the ratings. The authors first demonstrate that the proposed model successfully identifies segment-specific key drivers of customer satisfaction using illustrative simulated review data. Then, the authors utilize real-world reviews from Yelp for empirical applications. When applied to online reviews of 5,241 Arizona-based restaurants, the model identifies three distinct restaurant segments, each characterized by three to five important topics. The model's performance is evaluated against six benchmark models, encompassing various topic models and latent class regression with variable selection. The comparison results emphasize the proposed model's unique advantages in prediction, interpretability, and handling heterogeneity. Additionally, the authors demonstrate the applicability of the model in examining customer segmentation for individual restaurants. [ABSTRACT FROM AUTHOR]

Two machine-learning-aided thermodynamic integration schemes to compute the chemical potentials of atoms and molecules have been developed and compared. One is the particle insertion method, and the other combines particle insertion with element substitution. In the former method, the species is gradually inserted into the liquid and its chemical potential is computed. In the latter method, after the particle insertion, the inserted species is substituted with another species, and the chemical potential of this new species is computed. In both methods, the thermodynamic integrations are conducted using machine-learned potentials trained on first-principles datasets. The errors of the machine-learned surrogate models are further corrected by performing thermodynamic integrations from the machine-learned potentials to the first-principles potentials, accurately providing the first-principles chemical potentials. These two methods are applied to compute the real potentials of proton, alkali metal cations, and halide anions in water. The applications indicate that these two entirely different thermodynamic pathways yield identical real potentials within statistical error bars, demonstrating that both methods provide reproducible real potentials. The computed real potentials and solvation structures are also in good agreement with past experiments and simulations. These results indicate that machine-learning surrogate models enabling particle insertion and element substitution provide a precise method for determining the chemical potentials of atoms and molecules. [ABSTRACT FROM AUTHOR]

This study examines the application of machine learning algorithms, specifically the Random Forest regression model, to optimize the magnetocaloric effect in all- d -metal Heusler alloys. The model was trained using descriptors related to the mean properties of individual atoms, the properties of simple compounds in their ground state, and measures of chemical disorder. It demonstrated high accuracy in predicting structural properties, while exhibiting moderate accuracy in predicting magnetic properties. To identify optimal alloy compositions, a genetic algorithm was used to find those with the greatest differences in magnetization during martensitic transitions. Using this combined approach, the Ni–Co–Mn–Ti alloy system was thoroughly explored, resulting in the discovery of an alloy with a maximum magnetization difference. These results are consistent with previous research based on density functional theory and highlight the effectiveness of integrating machine learning with genetic algorithms for the discovery of new materials with outstanding magnetocaloric properties. The study emphasizes the need for further refinement of models capable of accurately predicting complex magnetic interactions, which is essential for fully leveraging the potential of all- d -metal Heusler alloys in practical applications. [ABSTRACT FROM AUTHOR]

We numerically investigate the identification of relevant structural features that contribute to the dynamical heterogeneity in a model glass-forming liquid. By employing the recently proposed information imbalance technique, we select these features from a range of physically motivated descriptors. This selection process is performed in a supervised manner (using both dynamical and structural data) and an unsupervised manner (using only structural data). We then apply the selected features to predict future dynamics using a machine learning technique. One of the advantages of the information imbalance technique is that it does not assume any model a priori, i.e., it is a non-parametric method. Finally, we discuss the potential applications of this approach in identifying the dominant mechanisms governing the glassy slow dynamics. [ABSTRACT FROM AUTHOR]

The chemical reactions between certain interstellar molecules are exothermic in nature and barrierless in the entrance channel, allowing these reactions to occur rapidly even at low astronomical temperatures, e.g., C and O2 interaction. Obtaining detailed rovibrational transition parameters for the reaction between C and O2, such as state-selected rate coefficients, is crucial for studying the associated atmospheric and astronomical environments. Hence, this work presents an approach that combines quasi-classical trajectory calculations with machine learning techniques based on Neural Network (NN) and Gaussian Process Regression (GPR) to determine state-selected rate coefficients. Employing this approach, we significantly reduced the computational requirements while simultaneously obtaining the accurate state-selected reaction cross sections and rate coefficients for the collision of C and O2. Both the NN-based and GPR-based models established in this work accurately predict the results calculated from explicit numerical calculations in the explored temperature range of 50–1500 K, achieving a coefficient of determination R2 > 0.96. Most importantly, the current work provides the most comprehensive dataset of rovibrational rate coefficients of v = 0–4, j = 0–70 → v′ = 0–15 for the astrophysical modeling of the C–O2 collision system. [ABSTRACT FROM AUTHOR]

As a demonstration of how fundamental chemical concepts can be gleaned from data using machine learning methods, we demonstrate the automated detection of hydrogen bonds by statistical analysis of molecular dynamics trajectories. In particular, we infer the existence and nature of electrostatically driven noncovalent interactions by examining the relative probability of supramolecular configurations with and without electrostatic interactions. Then, using Laplacian eigenmaps clustering, we identify hydrogen bonding motifs in hydrogen fluoride, water, and methanol. The hydrogen bonding motifs that we identify support traditional geometric criteria. [ABSTRACT FROM AUTHOR]

Interfacial fluids are ubiquitous in systems ranging from biological membranes to chemical droplets and exhibit a complex behavior due to their nonlinear, multiphase, and multicomponent nature. The development of accurate coarse-grained (CG) models for such systems poses significant challenges, as these models must effectively capture the intricate many-body interactions, both inter- and intramolecular, arising from atomic-level phenomena, and account for the diverse density distributions and fluctuations at the interface. In this study, we use advanced machine learning techniques incorporating force matching and diffusion probabilistic models to construct a robust CG model of interfacial fluids. We evaluate our model through simulations in various settings, including the water–air interface, bulk decane, and dipalmitoylphosphatidylcholine monolayer membranes. Our results show that our CG model accurately reproduces the essential many-body and interfacial properties of interfacial fluids and proves effective across different CG mapping strategies. This work not only validates the utility of our model for multiscale simulations, but also lays the groundwork for future improvements in the simulation of complex interfacial systems. [ABSTRACT FROM AUTHOR]

Machine-learning interatomic potential models based on graph neural network architectures have the potential to make atomistic materials modeling widely accessible due to their computational efficiency, scalability, and broad applicability. The training datasets for many such models are derived from density-functional theory calculations, typically using a semilocal exchange-correlation functional. As a result, long-range interactions such as London dispersion are often missing in these models. We investigate whether this missing component can be addressed by combining a graph deep learning potential with semiempirical dispersion models. We assess this combination by deriving the equations of state for layered pnictogen chalcohalides BiTeBr and BiTeI and performing crystal structure optimizations for a broader set of V–VI–VII compounds with various stoichiometries, many of which possess van der Waals gaps. We characterize the optimized crystal structures by calculating their x-ray diffraction patterns and radial distribution function histograms, which are also used to compute Earth mover's distances to quantify the dissimilarity between the optimized and corresponding experimental structures. We find that dispersion-corrected graph deep learning potentials generally (though not universally) provide a more realistic description of these compounds due to the inclusion of van der Waals attractions. In particular, their use results in systematic improvements in predicting not only the van der Waals gap but also the layer thickness in layered V–VI–VII compounds. Our results demonstrate that the combined potentials studied here, derived from a straightforward approach that neither requires fine-tuning the training nor refitting the potential parameters, can significantly improve the description of layered polar crystals. [ABSTRACT FROM AUTHOR]

Unlike with the energy, which is a scalar property, machine learning (ML) prediction of vector or tensor properties poses the additional challenge of achieving proper invariance (covariance) with respect to molecular rotation. For the energy gradients needed in molecular dynamics (MD), this symmetry is automatically fulfilled when taking analytic derivative of the energy, which is a scalar invariant (using properly invariant molecular descriptors). However, if the properties cannot be obtained by differentiation, other appropriate methods should be applied to retain the covariance. Several approaches have been suggested to properly treat this issue. For nonadiabatic couplings and polarizabilities, for example, it was possible to construct virtual quantities from which the above tensorial properties are obtained by differentiation and thus guarantee the covariance. Another possible solution is to build the rotational equivariance into the design of a neural network employed in the model. Here, we propose a simpler alternative technique, which does not require construction of auxiliary properties or application of special equivariant ML techniques. We suggest a three-step approach, using the molecular tensor of inertia. In the first step, the molecule is rotated using the eigenvectors of this tensor to its principal axes. In the second step, the ML procedure predicts the vector property relative to this orientation, based on a training set where all vector properties were in this same coordinate system. As the third step, it remains to transform the ML estimate of the vector property back to the original orientation. This rotate–predict–rotate (RPR) procedure should thus guarantee proper covariance of a vector property and is trivially extensible also to tensors such as polarizability. The RPR procedure has an advantage that the accurate models can be trained very fast for thousands of molecular configurations, which might be beneficial where many training sets are required (e.g., in active learning). We have implemented the RPR technique, using the MLatom and Newton-X programs for ML and MD, and performed its assessment on the dipole moment along MD trajectories of 1,2-dichloroethane. [ABSTRACT FROM AUTHOR]

In insurance, proposing an accurate premium that is adjusted to the insured risk profile allows companies to better manage their portfolios and to be more competitive. Machine learning methods have recently been adopted for various improvements in insurance ratemaking, especially in the automobile industry. These models are specifically used to mine potential data information and to build a predictive model for a variable of interest using explanatory variables. In this paper, we aim to provide a pricing method for ratemaking individual healthcare insurance contracts using machine learning algorithms that are applied to a Tunisian healthcare insurance portfolio. We start with a simple Classification and Regression Tree, and we work toward more advanced methods that are Random Forest, Extreme gradient boosting, Support Vector Regression, and Artificial Neural network regression model. The predictive performance of these non-parametric methods is compared with the standard generalized linear model. Our results showed the applicability of machine learning in the healthcare insurance market and that the XGBoost algorithm outperforms the predictive capacity of the classical generalized linear model. [ABSTRACT FROM AUTHOR]

We develop an algorithm for solving a large class of nonlinear high-dimensional continuous-time models in finance. We approximate value and policy functions using deep learning and show that a combination of automatic differentiation and Ito's lemma allows for the computation of exact expectations, resulting in a negligible computational cost that is independent of the number of state variables. We illustrate the applicability of our method to problems in asset pricing, corporate finance, and portfolio choice and show that the ability to solve high-dimensional problems allows us to derive new economic insights. [ABSTRACT FROM AUTHOR]

The document discusses the integration of machine learning (ML) into thermal transport research, highlighting its transformative impact on understanding and controlling heat transfer processes. It features 31 papers categorizing ML applications into machine learning potentials, predicting thermal properties, design and optimization, data analysis, and tutorials. ML has enabled accurate simulations, precise property predictions, innovative system designs, and efficient data analysis in thermal transport research, showcasing the potential for further advancements in the field. Despite challenges like model transferability, data scarcity, and interpretability, the document emphasizes the promising future of ML in advancing thermal science and engineering. [Extracted from the article]

The Mpemba effect describes the phenomenon that a system at hot initial temperature cools faster than at an initial warm temperature in the same environment. Such an anomalous cooling has recently been predicted and realized for trapped colloids. Here, we investigate the freezing behavior of a passive colloidal particle by employing numerical Brownian dynamics simulations and theoretical calculations with a model that can be directly tested in experiments. During the cooling process, the colloidal particle exhibits multiple non-monotonic regimes in cooling rates, with the cooling time decreasing twice as a function of the initial temperature—an unexpected phenomenon we refer to as the Double Mpemba effect. In addition, we demonstrate that both the Mpemba and Double Mpemba effects can be predicted by various machine-learning methods, which expedite the analysis of complex, computationally intensive systems. [ABSTRACT FROM AUTHOR]

Looking back at seven decades of highly extensive application in the semiconductor industry, silicon and its native oxide SiO2 are still at the heart of several technological developments. Recently, the fabrication of ultra-thin oxide layers has become essential for keeping up with trends in the down-scaling of nanoelectronic devices and for the realization of novel device technologies. With this comes a need for better understanding of the atomic configuration at the Si/SiO2 interface. Classical force fields offer flexible application and relatively low computational costs, however, suffer from limited accuracy. Ab initio methods give much better results but are extremely costly. Machine learning force fields (MLFF) offer the possibility to combine the benefits of both worlds. We train a MLFF for the simulation of the dry thermal oxidation process of a Si substrate. The training data are generated by density functional theory calculations. The obtained structures are in line with ab initio simulations and with experimental observations. Compared to a classical force field, the most recent reactive force field, the resulting configurations are vastly improved. Our potential is publicly available in an open-access repository. [ABSTRACT FROM AUTHOR]

Accurately predicting spectra for heavy elements, often open-shell systems, is a significant challenge typically addressed using a single cluster approach with a fixed coordination number. Developing a realistic model that accounts for temperature effects, variable coordination numbers, and interprets experimental data is even more demanding due to the strong solute–solvent interactions present in solutions of heavy metal cations. This study addresses these challenges by combining multiple methodologies to accurately predict realistic spectra for highly charged metal cations in aqueous media, with a focus on the electronic absorption spectrum of Ce3+ in water. Utilizing highly correlated relativistic quantum mechanical (QM) wavefunctions and structures from molecular dynamics (MD) simulations, we show that the convolution of individual vertical transitions yields excellent agreement with experimental results without the introduction of empirical broadening. Good results are obtained for both the normalized spectrum and that of absolute intensity. The study incorporates a statistical machine learning algorithm, Gaussian Mixture Models-Nuclear Ensemble Approach (GMM-NEA), to convolute individual spectra. The microscopic distribution provided by MD simulations allows us to examine the contributions of the octa- and ennea-hydrate of Ce3+ in water to the final spectrum. In addition, the temperature dependence of the spectrum is theoretically captured by observing the changing population of these hydrate forms with temperature. We also explore an alternative method for obtaining statistically representative structures in a less demanding manner than MD simulations, derived from QM Wigner distributions. The combination of Wigner-sampling and GMM-NEA broadening shows promise for wide application in spectroscopic analysis and predictions, offering a computationally efficient alternative to traditional methods. [ABSTRACT FROM AUTHOR]

The energetically most favorable chemical ordering of bimetallic nanoparticles can be characterized by combining global optimization algorithms and surrogate energy models. The latter approximate the energy of nanoalloys relying on structural descriptors, training models, and data. Here, we systematically evaluate the performance of highly data-efficient topological descriptors [Kozlov et al., Chem. Sci. 6, 3868 (2015)] for predicting the energies of metal nanoalloys with different chemical orderings. We also introduce a new descriptor based on atomic coordination types, which results in a less data-efficient and interpretable approach, but improves the general accuracy and the quantification of orderings in the inner parts of nanoparticles. The capacity of both the original and new approaches in combination with a basin hopping algorithm is illustrated by generating convex hulls of PdZn nanoalloys and predicting the resulting active surface site distribution as a function of particle composition. Finally, we show how these approaches can be combined with machine-learning adsorption models in electrocatalysis studies for a fast evaluation of the reactivity landscape of targeted nanoalloys. [ABSTRACT FROM AUTHOR]

Cobalt monoxide (CoO) nanomaterials have drawn attention for their remarkable photocatalytic water splitting without an externally applied potential or co-catalyst. The success of overall water splitting is due to the appropriate band edge positions of the catalyst, which span the redox potentials of water splitting. Typically, CoO nanomaterials possess complex morphologies, which consist of multiple active surfaces. As a result, the precise roles of the surfaces in the overall water-splitting process remain to be elucidated. In this work, we have undertaken a thorough investigation into the band alignments at the CoO(100)–water and CoO(111)–water interfaces using ab initio molecular dynamics and machine learning accelerated molecular dynamics simulations. The results of band alignment reveal that CoO(100) supports both the Hydrogen Evolution Reaction (HER) and the oxygen evolution reaction, whereas CoO(111) only facilitates the HER. Moreover, the variance in band positions between CoO(100) and CoO(111) results in an intrinsic potential difference, facilitating the migration of electrons toward CoO(100), while holes accumulate on CoO(111). The separation of photoexcited carriers effectively promotes water splitting in CoO. [ABSTRACT FROM AUTHOR]

We investigate the density isobar of water and the melting temperature of ice using six different density functionals. Machine-learning potentials are employed to ensure computational affordability. Our findings reveal significant discrepancies between various base functionals. Notably, even the choice of damping can result in substantial differences. Overall, the outcomes obtained through density functional theory are not entirely satisfactory across most utilized functionals. All functionals exhibit significant deviations either in the melting temperature or equilibrium volume, with most of them even predicting an incorrect volume difference between ice and water. Our heuristic analysis indicates that a hybrid functional with 25% exact exchange and van der Waals damping averaged between zero and Becke–Johnson dampings yields the closest agreement with experimental data. This study underscores the necessity for further enhancements in the treatment of van der Waals interactions and, more broadly, density functional theory to enable accurate quantitative predictions for molecular liquids. [ABSTRACT FROM AUTHOR]

Quiet eye (QE), the visual fixation on a target before initiation of a critical action, is associated with improved performance. While QE is trainable, it is unclear whether QE can directly predict performance, which has implications for training interventions. This study predicted basketball shot outcome (make or miss) from visuomotor control variables using a decision tree classification approach. Twelve basketball athletes completed 200 shots from six on-court locations while wearing mobile eye-tracking glasses. Training and testing data sets were used for modeling eight predictors (shot location, arm extension time, and absolute and relative QE onset, offset, and duration) via standard and conditional inference decision trees and random forests. On average, the trees predicted over 66% of makes and over 50% of misses. The main predictor, relative QE duration, indicated success for durations over 18.4% (range: 14.5%–22.0%). Training to prolong QE duration beyond 18% may enhance shot success. [ABSTRACT FROM AUTHOR]

In this contribution, we propose machine learning techniques to predict zombie firms. First, we derive the risk of failure by training and testing our algorithms on disclosed financial information and nonrandom missing values of 304,906 firms active in Italy from 2008 to 2017. We then identify the highest financial distress conditional on predictions that lie above a threshold for which a combination of the false positive rate (false prediction of firm failure) and the false negative rate (false prediction of active firms) is minimized. Therefore, we identify zombies as firms that remain in financial distress, i.e. whose forecasts fall into the risk category above the threshold for at least three consecutive years. To this end, we implement a gradient boosting algorithm (XGBoost) that exploits information about missing values. The inclusion of missing values in our prediction model is crucial because patterns of undisclosed accounts are correlated with firm failure. Finally, we show that our preferred machine learning algorithm outperforms (i) proxy models such as Z -scores and the distance-to-default, (ii) traditional econometric methods, and (iii) other widely used machine learning techniques. We provide evidence that zombies are less productive and smaller on average and that they tend to increase in times of crisis. Finally, we argue that our application can help financial institutions and public authorities design evidence-based policies—e.g. optimal bankruptcy laws and information disclosure policies. [ABSTRACT FROM AUTHOR]

Excessive monetary compensation and existing contractual agreements of influencers limit the ability of many firms to engage in effective influencer seeding. The authors suggest a forward-looking approach of targeting prospective influencers —while they are still largely unknown (e.g., a few months after their platform registration)—and signing them to endorse the firm in the future (e.g., more than a year later). This approach has the potential to significantly reduce costs. However, as only rarely do newly registered users ultimately become influencers (and as signals are weak), the authors propose a novel framework to cope with this rare-event problem. For empirical demonstration and application, the authors conduct data-based simulations using a data set from a worldwide leading audio platform. Every wave of newly registered users is associated with a profit potential stemming from future endorsements by prospective influencers. With knowledge about the order of magnitude of the return on successful influencer spend, managers applying the framework can extract around 20% of this profit potential (if the return is around three times the spend). [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, many production scenarios utilise sensors to monitor manufacturing processes, resulting in massive data that can be leveraged to build machine-learning models to improve production efficiency and quality. In semiconductor manufacturing, where many sensors are employed to monitor the production process, the resulting multi-sensor data poses challenges for constructing a virtual metrology (VM) model to assess wafer quality. Furthermore, building separate prediction models for each sensor can result in a loss of redundancy present in the multi-sensor data. Therefore, this paper proposes a multi-source AdaBoost with a cross-weight sampling method for predicting the critical dimension required in VM. Compared with the existing AdaBoost algorithm in regression, the proposed method adapts well to multi-source data by incorporating a cross-weight sampling distribution. Thus, when updating the sampling weight distribution, the impact of both individual and multi-source data is considered to re-sample high-quality observations. Based on the numerical results from the synthetic datasets and case study in semiconductor manufacturing, the proposed method outperforms benchmark methods. Additionally, owing to the redundancy of multi-sensor data, the proposed method demonstrates robust performance regardless of the noise level in the semiconductor VM data. [ABSTRACT FROM AUTHOR]

Quality control is being increasingly automatised in the context of Industry 4.0. Its automatisation reduces inspection times and ensures the same criteria are used to evaluate all products. One of the challenges when developing supervised machine learning models is the availability of labelled data. Few-shot learning promises to be able to learn from few samples and, therefore, reduce the labelling effort. In this work, we combine this approach with unsupervised methods that learn anomaly maps on unlabelled data, providing additional information to the model and enhancing the classification models' discriminative capability. Our results show that the few-shot learning models achieve competitive results compared to those trained in a classical supervised classification setting. Furthermore, we develop novel active learning data sampling strategies to label an initial support set. The results show that using sampling strategies to create and label the initial support set yields better results than selecting samples at random. We performed the experiments on four datasets considering real-world data provided by Philips Consumer Lifestyle BV and Iber-Oleff - Componentes Tecnicos Em Plástico, S.A. [ABSTRACT FROM AUTHOR]

Solid–water interfaces are crucial to many physical and chemical processes and are extensively studied using surface-specific sum-frequency generation (SFG) spectroscopy. To establish clear correlations between specific spectral signatures and distinct interfacial water structures, theoretical calculations using molecular dynamics (MD) simulations are required. These MD simulations typically need relatively long trajectories (a few nanoseconds) to achieve reliable SFG response function calculations via the dipole moment–polarizability time correlation function. However, the requirement for long trajectories limits the use of computationally expensive techniques, such as ab initio MD (AIMD) simulations, particularly for complex solid–water interfaces. In this work, we present a pathway for calculating vibrational spectra (IR, Raman, and SFG) of solid–water interfaces using machine learning (ML)-accelerated methods. We employ both the dipole moment–polarizability correlation function and the surface-specific velocity–velocity correlation function approaches to calculate SFG spectra. Our results demonstrate the successful acceleration of AIMD simulations and the calculation of SFG spectra using ML methods. This advancement provides an opportunity to calculate SFG spectra for complicated solid–water systems more rapidly and at a lower computational cost with the aid of ML. [ABSTRACT FROM AUTHOR]

We combine ab initio molecular dynamics (AIMD) simulations with an unsupervised machine learning approach to automate the search for possible configurations of CO oxidation reaction intermediates catalyzed by the atomically dispersed Pt1/TiO2 catalyst. Following the example of Roncoroni and co-workers [Phys. Chem. Chem. Phys. 25, 13741 (2023)], we employ t-distributed stochastic neighbor embedding and hierarchical density-based spatial clustering of applications with noise to reduce the dimensionality and cluster AIMD snapshots based on the local coordination environment of Pt. We identify new local minima, particularly in cases where CO2 is bound to the active site, because it can coordinate in various ways with both the metal and support. The new minima constitute additional elementary steps in some proposed pathways for CO oxidation, resulting in turnover frequencies that differ from prior estimates by several orders of magnitude. This work, therefore, demonstrates that configuration sampling is a necessary component of computational studies of catalytic cycles for atomically dispersed catalysts. [ABSTRACT FROM AUTHOR]

We combine ab initio molecular dynamics (AIMD) simulations with an unsupervised machine learning approach to automate the search for possible configurations of CO oxidation reaction intermediates catalyzed by the atomically dispersed Pt1/TiO2 catalyst. Following the example of Roncoroni and co-workers [Phys. Chem. Chem. Phys. 25, 13741 (2023)], we employ t-distributed stochastic neighbor embedding and hierarchical density-based spatial clustering of applications with noise to reduce the dimensionality and cluster AIMD snapshots based on the local coordination environment of Pt. We identify new local minima, particularly in cases where CO2 is bound to the active site, because it can coordinate in various ways with both the metal and support. The new minima constitute additional elementary steps in some proposed pathways for CO oxidation, resulting in turnover frequencies that differ from prior estimates by several orders of magnitude. This work, therefore, demonstrates that configuration sampling is a necessary component of computational studies of catalytic cycles for atomically dispersed catalysts. [ABSTRACT FROM AUTHOR]

The ionic conductivity at the grain boundaries (GBs) in oxide ceramics is typically several orders of magnitude lower than that within the grain interior. This detrimental GB effect is the main bottleneck for designing high-performance ceramic electrolytes intended for use in solid-state lithium-ion batteries, fuel cells, and electrolyzer cells. The macroscopic ionic conductivity in oxide ceramics is essentially governed by the underlying polycrystalline microstructures where GBs and grain morphology go hand in hand. This provides the possibility to enhance the ion conductivity by microstructure engineering. To this end, a thorough understanding of microstructure–property correlation is highly desirable. In this work, we investigate numerous polycrystalline microstructure samples with varying grain and grain boundary features. Their macroscopic ionic conductivities are numerically evaluated by the finite element homogenization method, whereby the GB resistance is explicitly regarded. The influence of different microstructural features on the effective ionic conductivity is systematically studied. The microstructure–property relationships are revealed. Additionally, a graph neural network-based machine learning model is constructed and trained. It can accurately predict the effective ionic conductivity for a given polycrystalline microstructure. This work provides crucial quantitative guidelines for optimizing the ionic conducting performance of oxide ceramics by tailoring microstructures. [ABSTRACT FROM AUTHOR]

The urgent demand for high-performance emerging memory, propelled by artificial intelligence in internet of things (AIoT) and machine learning advancements, spotlights spin-transfer torque magnetic random-access memory as a prime candidate for practical application. However, magnetic tunnel junctions (MTJs) with a single-crystalline MgO barrier, which are central to magnetic random-access memory (MRAM), suffer from significant drawbacks: insufficient endurance due to breakdown and high writing power requirements. A superlattice barrier-based MTJ (SL-MTJ) is proposed to overcome the limitation. We first fabricated the MTJ using an SL barrier while examining the magnetoresistance and resistance-area product. Lower writing power can be achieved in SL-MTJs compared to MgO-MTJs. Our study may provide a new route to the development of MRAM technologies. [ABSTRACT FROM AUTHOR]

We developed an accurate and efficient machine learning potential with DFT accuracy and applied it to the silicon dry/wet oxidation process to investigate the underlying physics of thermal oxidation of silicon (001) surfaces. The accuracy of the potential was verified by comparing the melting point and structural properties of silicon, the structural properties of a-SiO2, and the adsorption properties on the silicon surface with experiment and DFT data. In subsequent thermal oxidation simulations, we successfully reproduced the accelerated growth phenomenon of the wet oxidation in the experiment, discussed the oxide growth process in detail, and elucidated that the accelerated growth is due to hydrogen in the system that both enhances the adsorption of oxygen on the silicon surface and promotes the migration of oxygen atoms. Finally, we annealed the oxidized structure, counted the defect information in the structure before and after annealing, and analyzed the defect evolution behavior during the annealing process. [ABSTRACT FROM AUTHOR]

Active matter systems, being in a non-equilibrium state, exhibit complex behaviors, such as self-organization, giving rise to emergent phenomena. There are many examples of active particles with biological origins, including bacteria and spermatozoa, or with artificial origins, such as self-propelled swimmers and Janus particles. The ability to manipulate active particles is vital for their effective application, e.g., separating motile spermatozoa from nonmotile and dead ones, to increase fertilization chance. In this study, we proposed a mechanism—an apparatus—to sort and demix active particles based on their motility values (Péclet number). Initially, using Brownian simulations, we demonstrated the feasibility of sorting self-propelled particles. Following this, we employed machine learning methods, supplemented with data from comprehensive simulations that we conducted for this study, to model the complex behavior of active particles. This enabled us to sort them based on their Péclet number. Finally, we evaluated the performance of the developed models and showed their effectiveness in demixing and sorting the active particles. Our findings can find applications in various fields, including physics, biology, and biomedical science, where the sorting and manipulation of active particles play a pivotal role. [ABSTRACT FROM AUTHOR]

The Europeana digital library features cultural heritage collections from over 3,000 European institutions described in 37 languages. However, most textual metadata describe the records in a single language, the data providers' language. Improving Europeana's multilingual accessibility presents challenges due to the unique characteristics of cultural heritage metadata, often expressed in short phrases and using in-domain terminology. This work presents the EuropeanaTranslate project's approach and results, aimed at translating Europeana metadata records from 23 EU languages into English. Machine Translation engines were trained on a cleaned selection of bilingual and synthetic data from Europeana, including multilingual vocabularies and relevant cultural heritage repositories. Automatic translations were evaluated through standard metrics and human assessments by linguists and domain cultural heritage experts. The results showed significant improvements when compared to the generic engines used before the in-domain training as well as the eTranslation service for most languages. The EuropeanaTranslate engines have translated over 29 million metadata records on Europeana.eu. Additionally, the MT engines and training datasets are publicly available via the European Language Grid Catalogue and the ELRC-SHARE repository. [ABSTRACT FROM AUTHOR]

The article discusses a common-sense approach for librarians in activating their intelligence in assessing and using artificial intelligence (AI). Cited are the ways on how they can sift through the challenges posed by AI and create a balanced approach drawing on the strengths of the library, and seven things that librarians should consider in supporting and helping people they serve activate their intelligence using AI as a tool.

AI-powered misinformation detectors—artificial intelligence tools that identify false or inaccurate online content—have emerged as a potential intervention for helping internet users understand the veracity of the content they view. However, the algorithms used to create these detectors are experimental and largely untested at the scale necessary to be effective on a social media platform. Furthermore, the assumptions programmed into these algorithms frequently conflict with everyday, non-threatening online communication, potentially leading to unwarranted censorship. Consequently, understanding the current limitations of AI interventions for misinformation detection on the internet is important for developing better iterations of these tools and forming effective policies around them. [ABSTRACT FROM AUTHOR]

Disinformation activities that aim to manipulate public opinion pose serious challenges to managing online platforms. One of the most widely used disinformation techniques is bot-assisted fake social engagement, which is used to falsely and quickly amplify the salience of information at scale. Based on agenda-setting theory, we hypothesize that bot-assisted fake social engagement boosts public attention in the manner intended by the manipulator. Leveraging a proven case of bot-assisted fake social engagement operation in a highly trafficked news portal, this study examines the impact of fake social engagement on the digital public's news consumption, search activities, and political sentiment. For that purpose, we used ground-truth labels of the manipulator's bot accounts, as well as real-time clickstream logs generated by ordinary public users. Results show that bot-assisted fake social engagement operations disproportionately increase the digital public's attention to not only the topical domain of the manipulator's interest (i.e., political news) but also to specific attributes of the topic (i.e., political keywords and sentiment) that align with the manipulator's intention. We discuss managerial and policy implications for increasingly cluttered online platforms. [ABSTRACT FROM AUTHOR]

In business research, firm size is both ubiquitous and readily measured. Complexity, another firm-related construct, is also relevant, but difficult to measure and not well-defined. As a result, complexity is less frequently incorporated in empirical designs. We argue that most extant measures of complexity are one-dimensional, have limited availability, and/or are frequently misspecified. Using both machine learning and an application-specific lexicon, we develop a text solution that uses widely available data and provides an omnibus measure of complexity. Our proposed measure, used in tandem with 10-K file size, provides a useful proxy that dominates traditional measures. [ABSTRACT FROM AUTHOR]

Machine learning classifiers are increasingly widely used. This research note explores how a particular widely used classifier, the Random Forest, performs when faced with samples which are imbalanced and noisy data. Both are known to affect accuracy, but if their effects are independent or not has not been explored. Based on an experiment using synthetic data generated for the study we find that the effects of noise and sample balance interact with each other; classification accuracy is worse when faced with both noisy data and sample imbalance. This has implications for the use of RF in market research, but also for how methods to address either sample imbalance or noise are assessed. [ABSTRACT FROM AUTHOR]

To maintain high-quality semiconductor wafer production processes, it is necessary to build high-quality virtual metrology (VM) model. Based on the result of the VM, the engineer can monitor and control the specific process. In the manufacturing process, various sources are obtained from sensor equipment. It is important to consider that these source data exhibit varying characteristics during the model construction. However, when all data sources are simply aggregated into a single model, the performance of the overall model may degrade, especially if any of the individual sources contain outliers or noise. To address this issue, we develop a data-fusion model designed to incorporate diverse data sources into a unified multi-source model. In particular, we improve an Adaboost regression algorithm to make it suitable for multi-source data in the field of VM. The algorithm combines the residuals of the models derived from each individual data source and all sources to adaptively adjust the thresholding value, which, in turn, determines whether the predicted values are accurate or not for each weak learner. Extensive practical validations on real-world processing data from semiconductor manufacturers have demonstrated that the proposed method outperforms single learning algorithms and is more robust than all benchmarks. [ABSTRACT FROM AUTHOR]

This study conducts a comprehensive analysis of deep reinforcement learning (DRL) algorithms applied to supply chain inventory management (SCIM), which consists of a sequential decision-making problem focussed on determining the optimal quantity of products to produce and ship across multiple capacitated local warehouses over a specific time horizon. In detail, we formulate the problem as a Markov decision process for a divergent two-echelon inventory control system characterised by stochastic and seasonal demand, also presenting a balanced allocation rule designed to prevent backorders in the first echelon. Through numerical experiments, we evaluate the performance of state-of-the-art DRL algorithms and static inventory policies in terms of both cost minimisation and training time while varying the number of local warehouses and product types and the length of replenishment lead times. Our results reveal that the Proximal Policy Optimization algorithm consistently outperforms other algorithms across all experiments, proving to be a robust method for tackling the SCIM problem. Furthermore, the (s, Q)-policy stands as a solid alternative, offering a compromise in performance and computational efficiency. Lastly, this study presents an open-source software library that provides a customisable simulation environment for addressing the SCIM problem, utilising a wide range of DRL algorithms and static inventory policies. [ABSTRACT FROM AUTHOR]

The drilling quality of carbon fibre reinforced polymer (CFRP) components is a key factor affecting the service life of the components, while energy saving and emission reduction in industrial production are crucial. In this study, drilling experiments were conducted on T300 plywood using a 55° coated tungsten steel drill bit, and CNN-LSTM neural network models were used to construct mapping relationships between process parameters (spindle speed, feed rate, and fibre lay-up sequence) and delamination factor and machine energy consumption. A new method of predicting the delamination factor by process parameters is proposed, and explored the optimal process parameter combinations that reduce the energy consumption of machine tools and minimise the delamination factor at the same time. The research results show that within the parameter settings, a spindle speed of 7000 r/min, a feed rate of 40 mm/min, and a lay-up sequence of [0°, 0°, −45°, 90°]6s ensure both low power consumption in the drilling process and the highest possible hole quality. This paper clearly demonstrates the feasibility of achieving low-power, high-quality drilling of CFRP through parameter optimisation, providing guidance to the manufacturing industry to improve the quality of CFRP hole-making while easing the pressure on carbon emissions. [ABSTRACT FROM AUTHOR]

The inventory management of medical items in humanitarian operations is a challenging task due to the intermittent nature of their demand and long replenishment lead-times. An effective response to humanitarian emergencies is often associated with excess inventories, which leads to high costs. Henceforth, using accurate demand forecasts to control the inventories of these medical items is shown to be a significant lever to reduce inventory costs while keeping high service levels. This paper investigates the effectiveness of parametric and non-parametric demand forecasting methods that are commonly considered to deal with items characterised with intermittent demand patterns. Moreover, we propose a new hybrid deep learning method that combines the long short-term memory neural network and the support vector regression. Such combination enables to deal with complex and non-linear data. We conduct an empirical investigation by means of data related to 523 medical items managed in two warehouses of a major humanitarian organisation. The forecast accuracy and the inventory efficiency of the methods are analysed. The empirical results show the high performance of the deep learning methods. These insights are valuable not only in the context of medical items in humanitarian operations, but also for any items with intermittent demand patterns. [ABSTRACT FROM AUTHOR]