Interfacial Optimization for AlN/Diamond Heterostructures via Machine Learning Potential Molecular Dynamics Investigation of the Mechanical Properties.

AlN/diamond heterostructures hold tremendous promise for the development of next-generation high-power electronic devices due to their ultrawide band gaps and other exceptional properties. However, the poor adhesion at the AlN/diamond interface is a significant challenge that will lead to film delamination and device performance degradation. In this study, the uniaxial tensile failure of the AlN/diamond heterogeneous interfaces was investigated by molecular dynamics simulations based on a neuroevolutionary machine learning potential (NEP) model. The interatomic interactions can be successfully described by trained NEP, the reliability of which has been demonstrated by the prediction of the cleavage planes of AlN and diamond. It can be revealed that the annealing treatment can reduce the total potential energy by enhancing the binding of the C and N atoms at interfaces. The strain engineering of AlN also has an important impact on the mechanical properties of the interface. Furthermore, the influence of the surface roughness and interfacial nanostructures on the AlN/diamond heterostructures has been considered. It can be indicated that the combination of surface roughness reduction, AlN strain engineering, and annealing treatment can effectively result in superior and more stable interfacial mechanical properties, which can provide a promising solution to the optimization of mechanical properties, of ultrawide band gap semiconductor heterostructures.

Phase Transition in Silicon from Machine Learning Informed Metadynamics.

Investigating reconstructive phase transitions in large-sized systems requires a highly efficient computational framework with computational cost proportional to the system size. Traditionally, widely used frameworks such as density functional theory (DFT) have been prohibitively expensive for extensive simulations on large systems that require long-time scales. To address this challenge, this study employed well-trained machine learning potential to simulate phase transitions in a large-size system. This work integrates the metadynamics simulation approach with machine learning potential, specifically deep potential, to enhance computational efficiency and accelerate the study of phase transition and consequent development of grains and dislocation defects in a system. The new method is demonstrated using the phase transitions of bulk silicon under high pressure. This approach has revealed the transition path and formation of polycrystalline silicon systems under specific stress conditions, demonstrating the effectiveness of deep potential-driven metadynamics simulations in gaining insights into complex material behaviors in large-sized systems.

The validity of learning potential for predicting educational adjustment in preschoolers with Down syndrome: A longitudinal study.

Learning Potential tests aim to assess cognitive functioning using mediation strategies by observing subsequent changes in learning patterns. In this study, this methodology was applied with the Preschool Learning Potential and Abilities Scale and two additional tests, the Kaufman Brief Intelligence Test and the Battery of Aptitudes for School Learning I, to a total of 58 children with Down Syndrome, at four and six years of age. The results demonstrate improvements in general intelligence, learning potential, and school aptitudes between the two timepoints. There was a significant, positive relationship between the variables measured in the different tests, as well as on the predictive variables of school aptitudes, in these children at six years of age. There is evidence that supports the use of this dynamic evaluation methodology, opening new fields of action in child evaluation processes.

Implementation and Validation of an OpenMM Plugin for the Deep Potential Representation of Potential Energy.

Machine learning potentials, particularly the deep potential (DP) model, have revolutionized molecular dynamics (MD) simulations, striking a balance between accuracy and computational efficiency. To facilitate the DP model's integration with the popular MD engine OpenMM, we have developed a versatile OpenMM plugin. This plugin supports a range of applications, from conventional MD simulations to alchemical free energy calculations and hybrid DP/MM simulations. Our extensive validation tests encompassed energy conservation in microcanonical ensemble simulations, fidelity in canonical ensemble generation, and the evaluation of the structural, transport, and thermodynamic properties of bulk water. The introduction of this plugin is expected to significantly expand the application scope of DP models within the MD simulation community, representing a major advancement in the field.

The Relationship between Learning Potential in Preschool Children and their Cognitive Abilities / La relación entre el potencial de aprendizaje en niños de preescolar y sus habilidades cognitivas

Learning potential can be used to assess and provide direction for action in diverse populations in modern educational settings. This study examined the relationship between the EHPAP dynamic evaluation scale and cognitive abilities (linguistic development, specific cognitive abilities, and executive functioning) in typically developing preschool children. Linear regression models indicated that children’s cognitive abilities formed part of the prediction of their independent performance in tasks before and after mediation. The abilities that formed part of the predictions were related to general linguistic development, specific cognitive functions, and executive functioning. Age as a predictive factor only contributed to verbal planning. This study examined the contribution of these variables in different EHPAP scales. (AU)

El potencial de aprendizaje se puede utilizar para evaluar y orientar la acción en diversas poblaciones en entornos educativos modernos. Este estudio analiza la relación entre la escala de evaluación dinámica EHPAP y las habilidades cognitivas (desarrollo lingüístico, habilidades cognitivas específicas y funcionamiento ejecutivo) en niños en edad preescolar con un desarrollo típico. Los modelos de regresión lineal indican que las habilidades cognitivas de los niños forman parte de la predicción de su desempeño independiente en tareas antes y después de la mediación. Las habilidades que formaban parte de las predicciones estaban relacionadas con el desarrollo lingüístico general, las funciones cognitivas específicas y el funcionamiento ejecutivo. La edad como factor predictivo solo contribuye a la planificación verbal. Este estudio analiza la contribución de estas variables en diferentes escalas EHPAP. (AU)

Training machine learning potentials for reactive systems: A Colab tutorial on basic models.

In the last several years, there has been a surge in the development of machine learning potential (MLP) models for describing molecular systems. We are interested in a particular area of this field - the training of system-specific MLPs for reactive systems - with the goal of using these MLPs to accelerate free energy simulations of chemical and enzyme reactions. To help new members in our labs become familiar with the basic techniques, we have put together a self-guided Colab tutorial (https://cc-ats.github.io/mlp_tutorial/), which we expect to be also useful to other young researchers in the community. Our tutorial begins with the introduction of simple feedforward neural network (FNN) and kernel-based (using Gaussian process regression, GPR) models by fitting the two-dimensional Müller-Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions (including the ANI variant) and embedding neural networks (such as DeepPot-SE). Lastly, these features will be fed into FNN and GPR models to reproduce the energies and forces for the molecular configurations in a Claisen rearrangement reaction.

Long Time CO2 Storage Under Ambient Conditions in Isolated Voids of a Porous Coordination Network Facilitated by the "Magic Door" Mechanism.

A coordination network containing isolated pores without interconnecting channels is prepared from a tetrahedral ligand and copper(I) iodide. Despite the lack of accessibility, CO2 is selectively adsorbed into these pores at 298 K and then retained for more than one week while exposed to the atmosphere. The CO2 adsorption energy and diffusion mechanism throughout the network are simulated using Matlantis, which helps to rationalize the experimental results. CO2 enters the isolated voids through transient channels, termed "magic doors", which can momentarily appear within the structure. Once inside the voids, CO2 remains locked in limiting its escape. This mechanism is facilitated by the flexibility of organic ligands and the pivot motion of cluster units. In situ powder X-ray diffraction revealed that the crystal structure change is negligible before and after CO2 capture, unlike gate-opening coordination networks. The uncovered CO2 sorption and retention ability paves the way for the design of sorbents based on isolated voids.

Ferroelectric Domain and Switching Dynamics in Curved In2Se3: First-Principles and Deep Learning Molecular Dynamics Simulations.

Despite its prevalence in experiments, the influence of complex strain on material properties remains understudied due to the lack of effective simulation methods. Here, the effects of bending, rippling, and bubbling on the ferroelectric domains are investigated in an In2Se3 monolayer by density functional theory and deep learning molecular dynamics simulations. Since the ferroelectric switching barrier can be increased (decreased) by tensile (compressive) strain, automatic polarization reversal occurs in α-In2Se3 with a strain gradient when it is subjected to bending, rippling, or bubbling deformations to create localized ferroelectric domains with varying sizes. The switching dynamics depends on the magnitude of curvature and temperature, following an Arrhenius-style relationship. This study not only provides a promising solution for cross-scale studies using deep learning but also reveals the potential to manipulate local polarization in ferroelectric materials through strain engineering.

Extremely Large Response of Phonon Coherence in Twisted Penta-NiN2 Bilayer.

Twisting has recently been demonstrated as an effective strategy for tuning the interactions between particles or quasi-particles in layered materials. Motivated by the recent experimental synthesis of pentagonal NiN2 sheet [ACS Nano 2021, 15, 13539], for the first time, the response of phonon coherence to twisting in bilayer penta-NiN2 , going beyond the particle-like phonon transport is studied. By using the unified theory of phonon transport and high order lattice anharmonicity, together with the self-consistent phonon theory, it is found that the lattice thermal conductivity is reduced by 80.6% from 33.35 to 6.47 W m-1 K-1 at 300 K when the layers are twisted. In particular, the contribution of phonon coherence is increased sharply by an order of magnitude, from 0.21 to 2.40 W m-1 K-1 , due to the reduced differences between the phonon frequencies and enhanced anharmonicity after the introduction of twist. The work provides a fundamental understanding of the phonon interaction in twisted pentagonal sheets.

Enhancing Immediate Memory, Potential Learning, and Working Memory with Transcranial Direct Current Stimulation in Healthy Older Adults.

BACKGROUND: Transcranial direct current stimulation (tDCS) has emerged as a prevention method or minimizer of the normal cognitive deterioration that occurs during the aging process. tDCS can be used to enhance cognitive functions such as immediate memory, learning, or working memory in healthy subjects. The objective of this study was to analyze the effect of two 20-min sessions of anodal transcranial direct stimulation on immediate memory, learning potential, and working memory in healthy older adults. METHODS: A randomized, single-blind, repeated-measures, sham-controlled design was used. The sample is made up of 31 healthy older adults, of whom 16 were in the stimulation group and 15 were in the sham group. The anode was placed on position F7, coinciding with the left dorsolateral prefrontal cortex region, and the cathode was placed on Fp2, the right supraorbital area (rSO). RESULTS: When comparing the results of the treatment group and the sham group, differences were observed in working memory and learning potential; however, no differences in immediate memory were found. CONCLUSION: The results showed that tDCS is a non-invasive and safe tool to enhance cognitive processes in healthy older adults interested in maintaining some cognitive function.

Exploring the Effects of Ionic Defects on the Stability of CsPbI3 with a Deep Learning Potential.

Inorganic metal halide perovskites, such as CsPbI3 , have recently drawn extensive attention due to their excellent optical properties and high photoelectric efficiencies. However, the structural instability originating from inherent ionic defects leads to a sharp drop in the photoelectric efficiency, which significantly limits their applications in solar cells. The instability induced by ionic defects remains unresolved due to its complicated reaction process. Herein, to explore the effects of ionic defects on stability, we develop a deep learning potential for a CsPbI3 ternary system based upon density functional theory (DFT) calculated data for large-scale molecular dynamics (MD) simulations. By exploring 2.4 million configurations, of which 7,730 structures are used for the training set, the deep learning potential shows an accuracy approaching DFT-level. Furthermore, MD simulations with a 5,000-atom system and a one nanosecond timeframe are performed to explore the effects of bulk and surface defects on the stability of CsPbI3 . This deep learning potential based MD simulation provides solid evidence together with the derived radial distribution functions, simulated diffraction of X-rays, instability temperature, molecular trajectory, and coordination number for revealing the instability mechanism of CsPbI3 . Among bulk defects, Cs defects have the most significant influence on the stability of CsPbI3 with a defect tolerance concentration of 0.32 %, followed by Pb and I defects. With regards to surface defects, Cs defects have the largest impact on the stability of CsPbI3 when the defect concentration is less than 15 %, whereas Pb defects act play a dominant role for defect concentrations exceeding 20 %. Most importantly, this machine-learning-based MD simulation strategy provides a new avenue to explore the ionic defect effects on the stability of perovskite-like materials, laying a theoretical foundation for the design of stable perovskite materials.

[School-related attitudes of children from educationally disadvantaged backgrounds in the corona pandemic]. / Schulbezogene Einstellungen von Kindern aus bildungsfernen Milieus in der Corona-Pandemie.

There is widespread consensus that distance learning in lockdown discriminates children and young people from educationally disadvantaged backgrounds in particular. The gap between the privileged and the left-behind, which is already wide open in Germany, is widening more and more as a result of the COVID 19 pandemic. Empirical evidence for this pandemic-related effect is, however, still scarce, and the corresponding studies rarely include students themselves, and even less so those who are taught at schools in challenging situations. This paper reports on a quantitative study of elementary schools in particularly disadvantaged settings during the lockdown in spring 2021. It asks about individual attitudes and learning experiences of students during homeschooling. The goal was not to define a group of adolescents as disadvantaged and thus as disconnected, but to empirically investigate which individual factors, framework conditions, and mechanisms of action lead to some of these students being more successful in learning during the lockdown than others with initially equal starting conditions. The data analyses revealed four stable clusters, which are based on different forms of student agency and show potentials and opportunities to support students from educationally disadvantaged backgrounds individually-in homeschooling and in face-to-face teaching.

Improving the accuracy of the neuroevolution machine learning potential for multi-component systems.

In a previous paper Fanet al(2021Phys. Rev.B104, 104309), we developed the neuroevolution potential (NEP), a framework of training neural network based machine-learning potentials using a natural evolution strategy and performing molecular dynamics (MD) simulations using the trained potentials. The atom-environment descriptor in NEP was constructed based on a set of radial and angular functions. For multi-component systems, all the radial functions between two atoms are multiplied by some fixed factors that depend on the types of the two atoms only. In this paper, we introduce an improved descriptor for multi-component systems, in which different radial functions are multiplied by different factors that are also optimized during the training process, and show that it can significantly improve the regression accuracy without increasing the computational cost in MD simulations.

High Thermal Conductivity of Wurtzite Boron Arsenide Predicted by Including Four-Phonon Scattering with Machine Learning Potential.

Materials with high thermal conductivity are of great importance to the thermal management of modern electronic devices. Recently, it was found that cubic boron arsenide (c-BAs) is a high thermal conductivity (κ) material with a value of ∼1300 W/(m·K) at room temperature (RT), where four-phonon scattering plays a crucial role in limiting the κ. In this work, with four-phonon scattering included, we find that the κ of wurtzite BAs (w-BAs) reaches as high as 1036 W/(m·K) along the a-b plane at RT, decreasing by 43% compared to the calculation without considering four-phonon scattering. The similar phonon transport properties between c-BAs and w-BAs can be understood in terms of similar projected density of states and scattering rates, which have the origin in crystal structural resemblance. To accelerate the calculation, the moment tensor potential derived from machine learning is adopted and proven to be a reliable and efficient method to obtain high-order interatomic force constants.

High-temperature phonon transport properties of SnSe from machine-learning interatomic potential.

As a promising thermoelectric material, tin selenide (SnSe) is of relatively low thermal conductivity. However, the phonon transport mechanisms in SnSe are not fully understood due to the complex phase transition, dynamical instability, and strong anharmonicity. In this work, we perform molecular dynamics simulations with a machine-learning interatomic potential to explore the thermal transport properties of SnSe at different temperatures. The developed interatomic potential is parameterized using the framework of moment tensor potential, exhibiting satisfactory predictions on temperature-dependent lattice constants and phonon dispersion, as well as phase transition temperature. From equilibrium molecular dynamics simulations, we obtained the thermal conductivity tensor from 200 K to 900 K. The origins of temperature-dependent thermal conductivity anisotropy and the roles of four-phonon scatterings are identified. The obtained interatomic potential can be utilized to study the mechanical and thermal properties of SnSe and related nanostructures in a wide range of temperatures.

Toward a sociocultural approach to computerized dynamic assessment of the TOEFL iBT listening comprehension test.

The current study addressed the impact of computerized dynamic assessment (C-DA) on the TOEFL iBT listening comprehension test administered to Iranian EFL learners (n = 185) who took part in preparation courses on the TOEFL exam in some language centres in Iran. To mediate the test-takers with hints to process the listening questions, a computer software program was developed, and it was meant to produce the following: Actual, mediated, and learning potential scores. Findings of the study indicated that the actual and mediated scores led to significant different mean scores in various listening ability levels in almost all question types. Generally, results highlighted the significant positive impact of C-DA on improving EFL test-takers' performances in the monologue and dialogue tasks. Teachers were recommended to implement C-DA since the information gained from this sociocultural assessment mode empowers them to provide learners with more individualized and accordingly more effective teaching and assessment strategies.

Programa de entrenamiento en potencial de aprendizaje para niños colombianos con dificultades de aprendizaje en Matemáticas / Dynamic assessment program for Colombian children with learning disabilities in Mathematics (LDM)

Resumen Las dificultades de aprendizaje en Matemáticas (DAM) han sido estudiadas por parte de los investigadores en los últimos años. La literatura sobre su clasificación y su identificación en niños ha sido relevante; sin embargo, es poca la bibliografía actual acerca de cómo superar dichas dificultades en niños. El presente estudio tuvo como objetivo la validación de un protocolo para mejorar habilidades en el campo de las matemáticas. La muestra se componía de 59 niños con esta característica, además del bajo rendimiento en dicha área. Los participantes tenían entre 7 y 10 años y estaban cursando entre segundo y cuarto año de primaria. El diseño es cuasiexperimental con medidas pretest-postest y grupo control en lista de espera. El programa constó de cinco semanas, con una sesión semanal de forma grupal con una duración de 50 minutos aproximadamente. Los instrumentos de evaluación fueron: TEDI-MATH para la identificación de las DAM y tres subpruebas de la WISC-IV, dígitos, conceptos y claves. Los resultados de la investigación arrojaron diferencias significativas entre el grupo control y experimental para todas las pruebas de la TEDI-MATH y para la subprueba de conceptos en WISC-IV. Los resultados son coherentes con otras investigaciones relacionadas con la comorbilidad entre los campos del lenguaje y las matemáticas. Se concluye que, al entrenar aspectos relacionados con la adquisición del lenguaje mejora la comprensión y el manejo de las competencias en matemáticas en los niños.

Abstract Learning Disabilities Mathematical LDM, have been the subject of study since the decade of the 70`s, advances in this field have allowed a correct identification and classification through the design of various instruments for this objective. The number of investigations that address intervention protocols to improve this type of difficulties in children is even more limited. On this way, comorbidities have also been found different researches with the Language Learning Disabilities LLD, and as well as their relationship with the neurological development of children. It is estimated that in countries like Germany, USA, and Spain, the percentage of children with LDM is between 3% and 8%. In particular, in Colombia at the moment, there is very little research that allows identifying these difficulties in children, as well as very little research on the different types of intervention to improve these difficulties in Math. In General, there are little current literature on how to overcome such difficulties in children. An intervention based on the perspective of dynamic assessment would be useful for children who have these difficulties. The dynamic assessment implies two important concepts: activity and modifiability. The tester and the inquired assume an active role, where the tester intervenes and modifies the interaction with the inquired with the purpose of successfully inducing learning. The inquired is directed and reinforced to assume an active role in searching and organizing information. The product of the assessment is modifiability or change in cognitive functioning. This study presents the validation of a protocol based on dynamic assessment to improve skills in the field of mathematics. The sample was 59 Colombian children from public schools in the city of Bogotá. Participants were between 7 to 10 years old and from second to fourth grade. They had Mathematics Learning Difficulties in evaluated with TEDI-MATH on short version. They were distributed in 28 boys and 31 girls with this characteristic. And children who were undergoing psychological, medicated and/or clinical treatment processes were excluded. The design used was quasi-experimental with pretest-posttest measures and control group on the waiting list. The program considered five weeks, with one weekly session of group with a duration of approximately 50 minutes. The evaluation instruments were: TEDI-MATH which was used for the identification of the MLD; 3 sub tests of the WISC-IV, digits, concepts and keys. And PAMATH-C protocol was used for the intervention with children with the aforementioned difficulties. The results of the investigation showed significant differences between the control and experimental group for all the TEDI-MATH tests and for the subtest of concepts in WISC-IV. The results are consistent with other research related to comorbidity between the fields of language and mathematics. The results showed that like other research based interventions with Learning Potential training programs are effective in improving the different metacognitive abilities among the participants. and has a significant effect on the decrease in Mathematical Learning Disabilities. This research leaves projections for an application to a larger population in Colombia and in South America of the TEDYMATH test on short version, since good discrimination was observed for children with LDM. The application of the PAMATH-C protocol in other types of rural and urban population throughout the territories will improve the identification of children with difficulties. In addition, the contribution for the design of new tests based on dynamic evaluation and its form of application to improve other types of difficulties in both children and adults. Therefore, an immense methodological field to develop future protocols based on the Learning Potential. It is concluded that when training aspects related to language acquisition, it improves the understanding and management of mathematics in children.

Learning curves, potential and speed in training of laparoscopic skills: a randomised comparative study in a box trainer.

BACKGROUND: The effectiveness of practical surgical training is characterised by an inherent learning curve. Decisive are individual initial starting capabilities, learning speed, ideal learning plateaus, and resulting learning potentials. The quantification of learning curves requires reproducible tasks with varied levels of difficulty. The hypothesis of this study is that the use of three-dimensional (3D) vision is more advantageous than two-dimensional vision (2D) for the learning curve in laparoscopic training. METHODS: Forty laparoscopy novices were recruited and randomised to a 2D Group and a 3D Group. A laparoscopy box trainer with two standardised tasks was used for training of surgical tasks. Task 1 was a positioning task, while Task 2 called for laparoscopic knotting as a more complex process. Each task was repeated at least ten times. Performance time and the number of predefined errors were recorded. 2D performance after 3D training was assessed in an additional final 2D cycle undertaken by the 3D Group. RESULTS: The calculated learning plateaus of both performance times and errors were lower for 3D. Independent of the vision mode the learning curves were smoother (exponential decay) and efficiency was learned faster than precision. The learning potentials varied widely depending on the corresponding initial values and learning plateaus. The final 2D performance time of the 3D-trained group was not significantly better than that of the 2D Group. The final 2D error numbers were similar for all groups. CONCLUSIONS: Stereoscopic vision can speed up laparoscopic training. The 3D learning curves resulted in better precision and efficiency. The 3D-trained group did not show inferior performance in the final 2D cycle. Consequently, we encourage the training of surgical competences like suturing and knotting under 3D vision, even if it is not available in clinical routine.

Development and evaluation of the Ingwavuma receptive vocabulary test: A tool for assessing receptive vocabulary in isiZulu-speaking preschool children.

BACKGROUND: This study used local resources- community members, photographer and speech therapists to develop a new test for screening receptive language skills and sought to determine its feasibility for use with a larger population in KwaZulu-Natal province, South Africa. OBJECTIVES: The aim of this study was to develop a one-word receptive vocabulary test appropriate for screening and diagnosis of isiZulu-speaking preschool-aged children. The objectives were (1) to determine sensitivity and specificity of the Ingwavuma Receptive Vocabulary Test (IRVT) and (2) to determine the relationship of IRVT scores with age, gender, time and the confounding variables of stunting and school. METHOD: The study was quantitative, cross-sectional and descriptive in nature. The IRVT was piloted before being administered to 51 children (4-6 years old). Statistical analysis of test item prevalence, correlations to confounding variables and validity measurements were conducted using Statistical Package for Social Scientists version 25 (SPSS 25). RESULTS: The IRVT was able to profile the receptive skills for the preschool children in Ingwavuma. The mean raw score for boys was 35, and 32 for girls. There was a significant Pearson correlation between test scores and age (0.028, p 0.05) with a high effect size (Cohen's d = 0. 949), gender (r = -0.032, p 0.05) with a medium effect size (Cohen's d = 0.521) and school (r = 0.033, p 0.05) with a small effect size (Cohen's d = 0.353). The sensitivity and specificity values were 66.7% and 33%, respectively. The test reliability (Cronbach's alpha) was 0.739, with a good test-retest reliability. CONCLUSION: The IRVT has potential as a screening test for isiZulu receptive vocabulary skills amongst preschool children. This study contributes to a development of clinical and research resources for assessing language abilities.

Developing and using hints in computerized dynamic assessment of a TOEFL iBT reading exam.

This study investigated the impacts of computerized dynamic assessment (C-DA) on a TOEFL iBT reading exam among 185 upper-intermediate EFL Iranian learners. The exam included five question types: Vocabulary, detail, negative fact, purpose, and inference items whose answers yielded three types of scores: Actual, mediated, and learning potential. Results indicated statistically significant differences between actual and mediated scores with various reading ability levels in using hints in the question types. Even though C-DA improved the scores in the mediated test items and resulted in significant correlations, there was no empirical evidence that C-DA was conducive to a comprehensive diagnosis of the ability in the Zone of Actual Development (ZAD). The study had direct pedagogical and methodological implications by suggesting more individualized and, accordingly, more effective mediation to learners, such as the interactionist approach.