The Impact of Utilizing Learning Centers to Promote STEM Development in the Early Childhood Classroom

This literature review explores the research on incorporating STEM in the preschool classroom, in particular, learning centers. Research from the past decade shows developing the four areas of science, technology, mathematics, and engineering (STEM) in the early years is beneficial for students’ science abilities in the later grades. Studies from this review shine a light on teacher attitudes about science, the classroom environment, and approaches to learning and how they influence students’ learning of STEM in preschool

Energy-Based Clustering: Fast and Robust Clustering of Data with Known Likelihood Functions

Clustering has become an indispensable tool in the presence of increasingly large and complex data sets. Most clustering algorithms depend, either explicitly or implicitly, on the sampled density. However, estimated densities are fragile due to the curse of dimensionality and finite sampling effects, for instance in molecular dynamics simulations. To avoid the dependence on estimated densities, an energy-based clustering (EBC) algorithm based on the Metropolis acceptance criterion is developed in this work. In the proposed formulation, EBC can be considered a generalization of spectral clustering in the limit of large temperatures. Taking the potential energy of a sample explicitly into account alleviates requirements regarding the distribution of the data. In addition, it permits the subsampling of densely sampled regions, which can result in significant speed-ups and sublinear scaling. The algorithm is validated on a range of test systems including molecular dynamics trajectories of alanine dipeptide and the Trp-cage miniprotein. Our results show that including information about the potential-energy surface can largely decouple clustering from the sampling density

Hybrid Classical/Machine-Learning Force Fields for the Accurate Description of Molecular Condensed-Phase Systems

Electronic structure methods offer in principle accurate predictions of molecular properties, however, their applicability is limited by computational costs. Empirical methods are cheaper, but come with inherent approximations and are dependent on the quality and quantity of training data. The rise of machine learning (ML) force fields (FFs) exacerbates limitations related to training data even further, especially for condensed-phase systems for which the generation of large and high-quality training datasets is difficult. Here, we propose a hybrid ML/classical FF model that is parametrized exclusively on high-quality ab initio data of dimers and monomers in vacuum but is transferable to condensed-phase systems. The proposed hybrid model combines our previous ML-parametrized classical model with ML corrections for situations where classical approximations break down, thus combining the robustness and efficiency of classical FFs with the flexibility of ML. Extensive validation on benchmarking datasets and experimental condensed-phase data, including organic liquids and small-molecule crystal structures, showcases how the proposed approach may promote FF development and unlock the full potential of classical FFs

Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems

Quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations have been developed to simulate molecular systems, where an explicit description of changes in the electronic structure is necessary. However, QM/MM MD simulations are computationally expensive compared to fully classical simulations as all valence electrons are treated explicitly and a self-consistent field (SCF) procedure is required. Recently, approaches have been proposed to replace the QM description with machine learned (ML) models. However, condensed-phase systems pose a challenge for these approaches due to long-range interactions. Here, we establish a workflow, which incorporates the MM environment as an element type in a high-dimensional neural network potential (HDNNP). The fitted HDNNP describes the potential-energy surface of the QM particles with an electrostatic embedding scheme. Thus, the MM particles feel a force from the polarized QM particles. To achieve chemical accuracy, we find that even simple systems require models with a strong gradient regularization, a large number of data points, and a substantial number of parameters. To address this issue, we extend our approach to a delta-learning scheme, where the ML model learns the difference between a reference method (DFT) and a cheaper semi-empirical method (DFTB). We show that such a scheme reaches the accuracy of the DFT reference method, while requiring significantly less parameters. Furthermore, the delta-learning scheme is capable of correctly incorporating long-range interactions within a cutoff of 1.4 nm. It is validated by performing MD simulations of retinoic acid in water and the interaction between S-adenoslymethioniat with cytosine in water. The presented results indicate that delta-learning is a promising approach for (QM)ML/MM MD simulations of condensed-phase systems

Graph Convolutional Neural Networks for (QM)ML/MM Molecular Dynamics Simulations

To accurately study chemical reactions in the condensed phase or within enzymes, both a quantum-mechanical description and sufficient configurational sampling is required to reach converged estimates. Here, quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations play an important role, providing QM accuracy for the region of interest at a decreased computational cost. However, QM/MM simulations are still too expensive to study large systems on longer time scales. Recently, machine learning (ML) models have been proposed to replace the QM description. The main limitation of these models lies in the accurate description of long-range interactions present in condensed-phase systems. To overcome this issue, a recent workflow has been introduced combining a semi-empirical method (i.e. density functional tight binding (DFTB)) and a high-dimensional neural network potential (HDNNP) in a $\Delta$-learning scheme. This approach has been shown to be capable of correctly incorporating long-range interactions within a cutoff of 1.4 nm. One of the promising alternative approaches to efficiently take long-range effects into account is the development of graph convolutional neural networks (GCNN) for the prediction of the potential-energy surface. In this work, we investigate the use of GCNN models -- with and without a $\Delta$-learning scheme -- for (QM)ML/MM MD simulations. We show that the $\Delta$-learning approach using a GCNN and DFTB and as baseline achieves competitive performance on our benchmarking set of solutes and chemical reactions in water. The method is additionally validated by performing prospective (QM)ML/MM MD simulations of retinoic acid in water and S-adenoslymethioniat interacting with cytosine in water. The results indicate that the $\Delta$-learning GCNN model is a valuable alternative for (QM)ML/MM MD simulations of condensed-phase systems

Solvating atomic level fine-grained proteins in supra-molecular level coarse-grained water for molecular dynamics simulations

Simulation of the dynamics of a protein in aqueous solution using an atomic model for both the protein and the many water molecules is still computationally extremely demanding considering the time scale of protein motions. The use of supra-atomic or supra-molecular coarse-grained (CG) models may enhance the computational efficiency, but inevitably at the cost of reduced accuracy. Coarse-graining solvent degrees of freedom is likely to yield a favourable balance between reduced accuracy and enhanced computational speed. Here, the use of a supra-molecular coarse-grained water model that largely preserves the thermodynamic and dielectric properties of atomic level fine-grained (FG) water in molecular dynamics simulations of an atomic model for four proteins is investigated. The results of using an FG, a CG, an implicit, or a vacuum solvent environment of the four proteins are compared, and for hen egg-white lysozyme a comparison to NMR data is made. The mixed-grained simulations do not show large differences compared to the FG atomic level simulations, apart from an increased tendency to form hydrogen bonds between long side chains, which is due to the reduced ability of the supra-molecular CG beads that represent five FG water molecules to make solvent-protein hydrogen bonds. But, the mixed-grained simulations are at least an order of magnitude faster than the atomic level one

Free energy calculations offer insights into the influence of receptor flexibility on ligand-receptor binding affinities

Docking algorithms for computer-aided drug discovery and design often ignore or restrain the flexibility of the receptor, which may lead to a loss of accuracy of the relative free enthalpies of binding. In order to evaluate the contribution of receptor flexibility to relative binding free enthalpies, two host-guest systems have been examined: inclusion complexes of α-cyclodextrin (αCD) with 1-chlorobenzene (ClBn), 1-bromobenzene (BrBn) and toluene (MeBn), and complexes of DNA with the minor-groove binding ligands netropsin (Net) and distamycin (Dist). Molecular dynamics simulations and free energy calculations reveal that restraining of the flexibility of the receptor can have a significant influence on the estimated relative ligand-receptor binding affinities as well as on the predicted structures of the biomolecular complexes. The influence is particularly pronounced in the case of flexible receptors such as DNA, where a 50% contribution of DNA flexibility towards the relative ligand-DNA binding affinities is observed. The differences in the free enthalpy of binding do not arise only from the changes in ligand-DNA interactions but also from changes in ligand-solvent interactions as well as from the loss of DNA configurational entropy upon restrainin

School phobia: Causes and counseling approaches

Recent studies have demonstrated that there is an incidence rate of seventeen out of one thousand students who are affected by school phobia (Kennedy, 1965). School phobia, the refusal to attend school, does not favor a particular sex, socio-economic group, birth order, or age (McDonald & Shepard, 1976). This fear not only reveals itself as an emotional problem, but often has physical symptoms as well. These students may have the physical symptoms of stomach aches, nausea, paleness, trembling, an inability to move, dizziness, and sleep disturbances. They may also be picky eaters because of these physical symptoms. It is a painful and frightening experience for children and it is also a frustrating problem for parents and school personnel

Poetik der Störung. ‚Christian Kracht‘ als Herausforderung für die literarische Öffentlichkeit

Die vorliegende Studie befasst sich mit Phänomenen der Störung in Christian Krachts OEuvre und fragt danach, wie das Unbehagen der literarischen Öffentlichkeit mit diesem Autor zu erklären ist. Dabei werden journalistische und literarische Texte sowie die Selbstinszenierungsweisen des Autors auf die Verfahren hin untersucht, die die Irritationen hervorrufen. Der Begriff „Störung“ wird in der Studie nicht negativ verstanden, sondern neutral als Irritation von Automatismen und Normalerwartungen definiert, denen durch den Regelverstoß das Potential innewohnt, die Reflexion von Normalitäts- und Akzeptabilitätsgrenzen anzustoßen. Die Studie nutzt Erkenntnisse kulturwissenschaftlicher Störungsforschung und einen darin verorteten diskursanalytischen Zugang, der Close Readings einzelner Texte in ihren Dispositiven verortet und hinsichtlich der zugehörigen Wertungsdiskurse beschreibt. Die Untersuchung kommt zu dem Ergebnis, dass Kracht diverse Verfahren künstlerischer Transgression kombinierend einsetzt: Die Studie unterscheidet (narrations-)ästhetische, semiotische und hermeneutische Formen der Störung sowie Verfahren der Diskusstörung und medienreflexiver Störung im Handlungs- sowie Symbolsystem Literatur. Die Untersuchung zeigt auf, dass diese Verfahren Krachts Poetik auch nach der Imperium-Debatte (2012) und den Kanonisierungstendenzen um den Autor in subtilerer Ausprägung kennzeichnen und diese in einem Spannungsverhältnis zu produktions- und rezeptionsseitigen werkpolitischen Entstörungstendenzen stehen