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A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds.

This paper introduces project-based learning (PBL) features for developing technological, curricular, and pedagogical supports to engage students in computational thinking (CT) through modeling. CT is recognized as the collection of approaches that  involve people in computational problem solving. CT supports students in deconstructing and reformulating a phenomenon such that it can be resolved using an information-processing agent (human or machine) to reach a scientifically appropriate explanation of a phenomenon. PBL allows students to learn by doing, to apply ideas, figure out how phenomena occur and solve challenging, compelling and complex problems. In doing so, students  take part in authentic science practices similar to those of professionals in science or engineering, such as computational thinking. This paper includes 1) CT and its associated aspects, 2) The foundation of PBL, 3) PBL design features to support CT through modeling, and 4) a curriculum example and associated student models to illustrate how particular design features can be used for developing high school physical science materials, such as an evaporative cooling unit to promote the teaching and learning of CT.

The motion of atoms in a solid always responds to cooling or heating in a way that is consistent with the symmetry of the given space group of the solid to which they belong 1 , 2 . When the atoms move, the electronic structure of the solid changes, leading to different physical properties. Therefore, the determination of where atoms are and what atoms do is a cornerstone of modern solid-state physics. However, experimental observations of atomic displacements measured as a function of temperature are very rare, because those displacements are, in almost all cases, exceedingly small 3 , 4 , 5 . Here we show, using a combination of diffraction techniques, that the hexagonal manganites RMnO 3 (where R is a rare-earth element) undergo an isostructural transition with exceptionally large atomic displacements: two orders of magnitude larger than those seen in any other magnetic material, resulting in an unusually strong magneto-elastic coupling. We follow the exact atomic displacements of all the atoms in the unit cell as a function of temperature and find consistency with theoretical predictions based on group theories. We argue that this gigantic magneto-elastic coupling in RMnO 3 holds the key to the recently observed magneto-electric phenomenon in this intriguing class of materials 6 .

Conventional applications of the Galvanostatic Intermittent Titration Technique (GITT) and EIS for estimating chemical diffusivity in battery electrodes face issues such as insufficient relaxation time to reach equilibrium, excessively long pulse durations that violate the short-time diffusion assumption, and the assumption of sequential electrode reaction and diffusion processes. In this work, a quasi-equilibrium criterion of 0.1 mV h−1 was applied to NMC622 electrodes, yielding 8–9 h relaxations below 3.8 V, but above 3.8 V, voltage decayed linearly and indefinitely, even upon discharging titration, showing unusual nonmonotonic relaxation behavior. The initial 36-s transients of a 10-min galvanostatic pulse and diffusion impedance in series with the electrode reaction yielded consistent diffusivity values. However, solid-state diffusion in spherical active particles within porous electrodes, where ambipolar diffusion occurs in the pore electrolyte with t+=0.3, requires a physics-based three-rail transmission line model (TLM). The corrected diffusivity may be three to four times higher. An analytic two-rail TLM approximating the three-rail numerical model was applied to temperature- and frequency-dependent EIS data. This approach mitigates parameter ambiguity and unphysical correlations in EIS. Physics-based EIS enables the identification of multistep energetics and the diagnosis of performance and degradation mechanisms.

ABSTRACT Hydrogen evolution reaction (HER) in acidic media has been spotlighted for hydrogen production since it is a favourable kinetics with the supplied protons from a counterpart compared to that within alkaline environment. However, there is no choice but to use a platinum‐based catalyst yet. As for a noble metal‐free electrocatalyst, incorporation of earth‐abundant transition metal (TM) atoms into nanocarbon platforms has been extensively adopted. Although a data‐driven methodology facilitates the rational design of TM‐anchored carbon catalysts, its practical application suffers from either a simplified theoretical model or the prohibitive cost and complexity of experimental data generation. Herein, an effective and facile catalyst design strategy is proposed based on machine learning (ML) and its model verification using electrochemical methods accompanied by density functional theory simulations. Based on a Bayesian genetic algorithm ML model, the Ni‐incorporated carbon quantum dots (Ni@CQD) loaded on a three‐dimensional reduced graphene oxide conductor are proposed as the best HER catalyst amongst the various TM‐incorporated CQDs under the optimal conditions of catalyst loading, electrode type, and temperature and pH of electrolyte. The ML results are validated with electrochemical experiments, where the Ni@CQD catalyst exhibited superior HER activity, requiring an overpotential of 151 mV to achieve 10 mA cm−2 with a Tafel slope of 52 mV dec−1 and impressive durability in acidic media up to 100 h. This methodology can provide an effective route for the rational design of highly active electrocatalysts for commercial applications. A novel strategy of rational design of transition metal‐based electrocatalyst by combining machine‐learning‐based catalyst optimization steps with experimental verification steps demonstrates that optimized single‐atom nickel incorporated carbon quantum dots presents outstanding electrocatalytic hydrogen production efficiency due to its higher carrier transfer rate evidenced by density functional theory calculation.

We face complex global issues such as climate change that challenge our ability as humans to manage them. Models have been used as a pivotal science and engineering tool to investigate, represent, explain, and predict phenomena or solve problems that involve multi-faceted systems across many fields. To fully explain complex phenomena or solve problems using models requires both systems thinking (ST) and computational thinking (CT). This study proposes a theoretical framework that uses modeling as a way to integrate ST and CT. We developed a framework to guide the complex process of developing curriculum, learning tools, support strategies, and assessments for engaging learners in ST and CT in the context of modeling. The framework includes essential aspects of ST and CT based on selected literature, and illustrates how each modeling practice draws upon aspects of both ST and CT to support explaining phenomena and solving problems. We use computational models to show how these ST and CT aspects are manifested in modeling.

An ability to engage in system thinking is necessary to understand complex problems. While many pre-college students use system modeling tools, there is limited evidence of student reasoning about causal relationships that interact in diverging and converging chains, and how these affect system behavior. A chemistry unit on gas phenomena was implemented in two successive years with 73 high school students. Although the phenomena could be explained with simple linear causal reasoning, many student models included surprising and problematic causal chains and non-linear patterns. Commonly, discussion about student models in classrooms and interviews focuses on individual causal relationships. However, in our experience, this can fail to bring to the surface conceptual issues that result from the combination of two or more relationships. Using interview data from 19 students, we looked for instances where students explained their conceptions and we identified questioning strategies that elicited these explanations. Questions that appeared most productive asked about distal relationships between nonadjacent variables in model substructures of three to four variables. In response to these questions, eight students expressed their thinking about relationship combinations, including unexpected reasoning that had not emerged during classroom instruction. Using exemplars from three interviews, we argue that students engage in complex causal reasoning that may be implicit and unexpected, and that if this is not recognized, it cannot be responded to during instruction. We suggest that using model substructures as a mutual visual referent along with a simple questioning strategy shows promise for helping students make their causal reasoning explicit.

Complicated structures consisting of multi-layers with a multi-modal array of device components, i.e ., so-called patterned multi-layers, and their corresponding circuit designs for signal readout and addressing are used to achieve a macroscale electronic skin (e-skin). In contrast to this common approach, we realized an extremely simple macroscale e-skin only by employing a single-layered piezoresistive MWCNT-PDMS composite film with neither nano-, micro-, nor macro-patterns. It is the deep machine learning that made it possible to let such a simple bulky material play the role of a smart sensory device. A deep neural network (DNN) enabled us to process electrical resistance change induced by applied pressure and thereby to instantaneously evaluate the pressure level and the exact position under pressure. The great potential of this revolutionary concept for the attainment of pressure-distribution sensing on a macroscale area could expand its use to not only e-skin applications but to other high-end applications such as touch panels, portable flexible keyboard, sign language interpreting globes, safety diagnosis of social infrastructures, and the diagnosis of motility and peristalsis disorders in the gastrointestinal tract.

Back cover image: The rational design of transition metal incorporated electrocatalyst for hydrogen evolution reaction is an effective way to produce economical hydrogen. However, the practical application of data‐driven methodology is limited due to the complexity of electrochemical systems. In article number cey2.70006, Kim and Sim et al. present the machine learning based facile strategy to optimize the catalyst and experimental conditions. The trained model accurately predicts experimental variables, which are validated by proton exchange membrane‐based water electrolysis system. This work provides insight into the simplified approach for the design optimization of machine learning‐assisted catalysts and systems.

Size and scale is a “big idea” in nanoscale science and engineering and is poorly understood by secondary students. This paper describes the design process, implementation, and evaluation of a 12-h instructional unit for size and scale, in a summer science camp for middle school students from a low SES public school district. Instructional activities were designed following a construct-centered design approach and included the use of microscopes, custom-made computer simulations, and 2-D and 3-D scale models. The unit followed a project-based instructional approach and was contextualized with the driving question, “How can nanotechnology keep me from getting sick?” Pre- and post-intervention interviews revealed that students significantly increased their qualitative and quantitative knowledge of the size of objects including atom, viruses, and cells, with an effect size of 0.8 for an overall metric. The campers closed the gap with private middle school students and on some measures surpassed high school students from the same district. The principle of “broad spectrum” curriculum and instruction – activities that target specific advanced understandings but simultaneously scaffold or support the learning of more fundamental, prerequisite ideas – was inductively generated from an analysis of the learning activities.