Material Representation Research Articles

Are you teaching “distillation” correctly in your chemistry classes? An educational reconstruction

Abstract Distillation is often taught at secondary level in chemistry classes. There are, however, several pitfalls in teaching and learning the topic. First, there is not enough accessible research on students’ conceptions regarding distillation, which makes it difficult for teachers and teacher educators to teach accordingly in school or university. Second, the scientific explanation of distillation, especially the separation of liquid-liquid mixtures, is much more complex than represented in school books or other learning material. Third, teachers understandably rely on the representation in school books and other materials when teaching distillation, so that inappropriate concepts may be transferred to students. In this article, we follow the model of educational reconstruction and illustrate with reference to chemistry textbooks, school books, our own research results, and other studies on students’ conceptions the three pitfalls named above. Thus, this article aims to provide support for teachers and teacher educators to structure lessons on distillation based on scientifically appropriate information and with regard to students’ conceptions.

A hybrid polymer–water peridynamics model for ballistic penetration damage of soft materials

Numerically understanding the dynamic response of soft materials subject to high-speed penetration is crucial for advancing transdermal delivery technologies and analyzing injuries related to ballistics. Due to the complexity in material correspondence models and the difficulty of simulating high-speed impact damage problems using traditional mesh-based numerical techniques, precisely capturing mechanical behaviors of soft materials following ballistic penetration remains challenging. This work proposes a novel hybrid polymer–water model within the peridynamics (PD) framework to accurately clarify the complex damage process of soft materials subject to ballistic penetration. We propose the hybrid interactions comprising of water–water, polymer–polymer, and polymer–water interactions, which embodies the subscale nature of various components and captures the constitutive features of soft materials. A novel decomposition scheme for the deformation gradient is proposed to overcome the limitations in traditional non-ordinary state-based PD where only homogeneous particles are involved in the influence domain, extending the scope of PD theory to mixed material systems. The superior effectiveness and applicability of proposed model are demonstrated through compression, shear, fracture tests. Regarding to the case of high-velocity penetration, both motion profiles of projectile and pressure profiles of gelatin after penetration can be captured in line with physics awareness. The proposed model offers a reasonable and practical representation of soft material from the perspective of a particle-based approach and enhances the potential applications of PD framework for simulating large deformation and dynamic response of soft materials.

Kendini Açıklamak: Göbekli Tepe Kültürü’nde Bir Köken Miti Yorumu (Antropomorfik Temalara Alternatif Bir Bakış)

The humans of the Göbekli Tepe Neolithic culture of the Upper Euphrates Basin left behind long-term settlements with surprising monumental structures and a rich set of symbolism. This study puts the various symbolic themes of this culture to its center, and attempts to offer a reasonable interpretation of how the people of this culture and period constructed a “reality” about themselves. When the themes of human head and phallus are positioned at the center of the interpretations of this period’s symbolism, it is possible to claim that the Neolithic people conceptualized/explained themselves through an complex mythological narrative. This paper also hopes to contribute to the disciplines of archaeology, anthropology, sociology and history of religions by asserting that the T-shaped pillars of the Göbekli Tepe Culture are the phalli of the ancestors representing these ancestors’ remaining fragment/aspect on “the world,” and thus, are the material representations of the “now” and “this world,” rather than mythological ancestors, supernatural beings or gods. In this context, it is possible to relate the T-shaped pillars with ancestor cult, and also an important part of the anthropomorphic statues and reliefs with the initiation rites in which a person gains status in society. One of the biggest goals of this study is to begin a discussion about what can be the main mythological narrative of this culture.

Chemical Learning Module Based on Multiple Representations of Redox Materials

This study aims to create and evaluate the effectiveness of a chemistry learning module based on various redox material representations. Research and development (R&D) is the methodology used in this research: using a 4D model. It consists of defining, designing, developing, and disseminating stages. The research was carried out until the development phase. The quality of the module was assessed by material experts, media experts, and reviewers (chemistry educators). The respondents are 32 students in first grade of Senior High School Science class. Quality assessment and student responses were made using a Likert and Guttman scale questionnaire sheet. The result shows that the percentage of objectives found by validating all aspects of the module's content by media, reviewers, and chemistry educators experts are 91.6%, 94.7%, and 81.25%, respectively, with excellent categories, and students responded positively to the module. The module is considered helpful as a learning media for redox topics.

NeuBTF: Neural fields for BTF encoding and transfer

Neural material representations are becoming a popular way to represent materials for rendering. They are more expressive than analytic models and occupy less memory than tabulated BTFs. However, existing neural materials are immutable, meaning that their output for a certain query of UVs, camera, and light vector is fixed once they are trained. While this is practical when there is no need to edit the material, it can become very limiting when the fragment of the material used for training is too small or not tileable, which frequently happens when the material has been captured with a gonioreflectometer. In this paper, we propose a novel neural material representation which jointly tackles the problems of BTF compression, tiling, and extrapolation. At test time, our method uses a guidance image as input to condition the neural BTF to the structural features of this input image. Then, the neural BTF can be queried as a regular BTF using UVs, camera, and light vectors. Every component in our framework is purposefully designed to maximize BTF encoding quality at minimal parameter count and computational complexity, achieving competitive compression rates compared with previous work. We demonstrate the results of our method on a variety of synthetic and captured materials, showing its generality and capacity to learn to represent many optical properties.

Machine Learning-Aided Property Prediction of Hybrid Organic–Inorganic Perovskites Using Hirshfeld Surface Representations of Crystal Structures

This paper describes a lightweight neural network (NN) to predict thermodynamic, electric, and electronic properties of hybrid organic–inorganic perovskites (HOIPs) using Hirshfeld surfaces as novel material representation for HOIPs. The neural network utilizes only a few Hirshfeld surface features (e.g., volume, surface area, globularity, and effective radius), along with qualitative and quantitative (mixed) variables, to predict the properties of HOIPs in a highly accelerated manner. Our use of Hirshfeld surface-based descriptors of HOIP crystals leads to a new metric for measuring the effective radius of an organic molecule within a given structure, which are proven to be highly effective features for efficient machine learning of crystalline materials’ properties. A detailed comparison between the crystal graph convolutional neural network (CGCNN) and the Hirshfeld surface-based neural network analysis via UMAP and HDBSCAN clustering is provided to assess the efficacy of these methods for different compound chemistries. It is shown that a combination of lower-order feature representation and a shallow lightweight neural network is capable of predicting material properties for HOIPs. Benchmarking against well-established denser crystal property prediction techniques such as the CGCNN and deeper graph attention layer graph neural network (deeper GATGNN) shows that our approach provides comparable and, in some cases, even superior predictive performance of properties such as formation energy, band gap, and electronic dielectric constant but all at much lower computational cost.

Materials cartography: A forward-looking perspective on materials representation and devising better maps

Machine learning (ML) is gaining popularity as a tool for materials scientists to accelerate computation, automate data analysis, and predict materials properties. The representation of input material features is critical to the accuracy, interpretability, and generalizability of data-driven models for scientific research. In this Perspective, we discuss a few central challenges faced by ML practitioners in developing meaningful representations, including handling the complexity of real-world industry-relevant materials, combining theory and experimental data sources, and describing scientific phenomena across timescales and length scales. We present several promising directions for future research: devising representations of varied experimental conditions and observations, the need to find ways to integrate machine learning into laboratory practices, and making multi-scale informatics toolkits to bridge the gaps between atoms, materials, and devices.

CyRSoXS: a GPU-accelerated virtual instrument for polarized resonant soft X-ray scattering.

Polarized resonant soft X-ray scattering (P-RSoXS) has emerged as a powerful synchrotron-based tool that combines the principles of X-ray scattering and X-ray spectroscopy. P-RSoXS provides unique sensitivity to molecular orientation and chemical heterogeneity in soft materials such as polymers and biomaterials. Quantitative extraction of orientation information from P-RSoXS pattern data is challenging, however, because the scattering processes originate from sample properties that must be represented as energy-dependent three-dimensional tensors with heterogeneities at nanometre to sub-nanometre length scales. This challenge is overcome here by developing an open-source virtual instrument that uses graphical processing units (GPUs) to simulate P-RSoXS patterns from real-space material representations with nanoscale resolution. This computational framework - called CyRSoXS (https://github.com/usnistgov/cyrsoxs) - is designed to maximize GPU performance, including algorithms that minimize both communication and memory footprints. The accuracy and robustness of the approach are demonstrated by validating against an extensive set of test cases, which include both analytical solutions and numerical comparisons, demonstrating an acceleration of over three orders of magnitude relative to the current state-of-the-art P-RSoXS simulation software. Such fast simulations open up a variety of applications that were previously computationally unfeasible, including pattern fitting, co-simulation with the physical instrument for operando analytics, data exploration and decision support, data creation and integration into machine learning workflows, and utilization in multi-modal data assimilation approaches. Finally, the complexity of the computational framework is abstracted away from the end user by exposing CyRSoXS to Python using Pybind. This eliminates input/output requirements for large-scale parameter exploration and inverse design, and democratizes usage by enabling seamless integration with a Python ecosystem (https://github.com/usnistgov/nrss) that can include parametric morphology generation, simulation result reduction, comparison with experiment and data fitting approaches.

Real-Time Simulation of Ground Vehicles on Deformable Terrain

Abstract Sophisticated modeling and simulation, based on rigid and flexible multibody dynamics, are nowadays a standard procedure in the design and analysis of vehicle systems and are widely adopted for on-road driving. Off-road driving for both terrestrial wheeled and tracked vehicles, as well as wheeled and legged robots and rovers for extra-terrestrial exploration pose additional modeling and simulation challenges, a primary one being that of the vehicle–terrain interaction, modeling of deformable terrain, and terramechanics in general. Techniques for modeling deformable terrain span an entire range varying in complexity, representation accuracy, and ensuing computational effort. While formulations such as fully resolved granular dynamics, continuum representation of granular material, or finite element can provide a high level of accuracy, they do so at a significant cost, even when the implementation leverages parallel computing and/or hardware accelerators. Real-time or faster than real-time terramechanics is a highly desired capability (in applications such as training of autonomous vehicles and robotic systems) or critical capability (in applications such as human-in-the-loop or hardware-in-the-loop). We present a real-time capable deformable soil implementation, extended from the soil contact model (SCM) developed at the German Aerospace Center which in turn can be viewed as a generalization of the Bekker-Wong and Janosi-Hanamoto semi-empirical models for soil interaction with arbitrary three-dimensional shapes and arbitrary contact patches. This SCM implementation is available, alongside more computationally intensive deformable soil representations, in the open-source multiphysics package Chrono. We describe the overall implementation and the features of the Chrono SCM model, the efficient underlying data structures, the current multicore parallelization aspects, and its scalability properties for concurrent simulation of multiple vehicles on deformable terrain.

An XGBoost Algorithm Based on Molecular Structure and Molecular Specificity Parameters for Predicting Gas Adsorption

In this paper, an improved Extreme Gradient Boosting (XGBoost) algorithm based on the Graph Isomorphic Network (GIN) for predicting the adsorption performance of metal-organic frameworks (MOFs) is developed. It is shown that the graph isomorphic layer of this algorithm can directly learn the feature representation of materials from the connection of atoms in MOFs. Then, XGBoost can be used to predict the adsorption performance of MOFs based on feature representation. In this sense, it is not only possible to achieve end-to-end prediction directly from the structure of MOFs to adsorption performance but also to ensure the accuracy of prediction. The comparison between Grand Canonical Monte Carlo (GCMC) simulation and prediction supports the performance and effectiveness of the proposed algorithm.

What is the relationship between philanthropy, materiality and public space?

AbstractThis article explores the dynamic between public space and material representations of philanthropy. It adapts the conception of public space in terms of layers: physical, code and content (Lessig, 2001; Németh, 2012). The article discusses physical forms of philanthropy and the codified norms, processes and relationships in material representations of philanthropy (code). To this end, part one examines how gifts of buildings and the memorialisation of philanthropy embody the philanthropic dominance of public space. Part two explores how the (re)appropriation of public space(s) encapsulates what happens in, and in relation to physical manifestations of philanthropy (content). Specifically; (i) the uses of spaces (ii) deliberation in public spaces; (iii) the decolonisation of philanthropic processes and practices; and (iv) the complementarity of public space and the public sphere. The article offers a novel heuristic for philanthropy and public space that can also inform conversations between development professionals and donors, and public debate.

Representations of Materials for Machine Learning

High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning the relations between composition, structure, and properties and by exploiting such relations for design. However, to build these connections, materials data must be translated into a numerical form, called a representation, that can be processed by an ML model. Data sets in materials science vary in format (ranging from images to spectra), size, and fidelity. Predictive models vary in scope and properties of interest. Here, we review context-dependent strategies for constructing representations that enable the use of materials as inputs or outputs for ML models. Furthermore, we discuss how modern ML techniques can learn representations from data and transfer chemical and physical information between tasks. Finally, we outline high-impact questions that have not been fully resolved and thus require further investigation.

Artificial Intelligence Guided Studies of van der Waals Magnets

AbstractA materials informatics framework to explore a large number of candidate van der Waals (vdW) materials is developed. In particular, in this study a large space of monolayer transition metal halides is investigated by combining high‐throughput density functional theory calculations and artificial intelligence (AI) to accelerate the discovery of stable materials and the prediction of their magnetic properties. The formation energy is used as a proxy for chemical stability. Semi‐supervised learning is harnessed to mitigate the challenges of sparsely labeled materials data in order to improve the performance of AI models. This approach creates avenues for the rapid discovery of chemically stable vdW magnets by leveraging the ability of AI to recognize patterns in data, to learn mathematical representations of materials from data and to predict materials properties. Using this approach, previously unexplored vdW magnetic materials with potential applications in data storage and spintronics are identified.

Synthesising 3D solid models of natural heterogeneous materials from single sample image, using encoding deep convolutional generative adversarial networks

Three-dimensional solid computational representations of natural heterogeneous materials are challenging to generate due to their high degree of randomness and varying scales of patterns, such as veins and cracks, in different sizes and directions. In this regard, this paper introduces a new architecture to synthesise 3D solid material models by using encoding deep convolutional generative adversarial networks (EDCGANs). DCGANs have been useful in generative tasks in relation to image processing by successfully recreating similar results based on adequate training. While concentrating on natural heterogeneous materials, this paper uses an encoding and a decoding DCGAN combined in a similar way to auto-encoders to convert a given image into marble, based on patches. Additionally, the method creates an input dataset from a single 2D high-resolution exemplar. Further, it translates of 2D data, used as a seed, into 3D data to create material blocks. While the results on the Z-axis do not have size restrictions, the X- and Y-axis are constrained by the given image. Using the method, the paper explores possible ways to present 3D solid textures. The modelling potentials of the developed approach as a design tool is explored to synthesise a 3D solid texture of leaf-like material from an exemplar of a leaf image.

Stethoscope and the History of Its Creation in the Reflection of Philately and Numismatics

The article presents research materials concerning the study of representation in the collection materials, necessary information about the French physician, the author of the method of auscultation, René Laennec. He is described through philatelic materials, as well as through commemorative and table medals. The text is richly illustrated with illustrative material, explanatory descriptions and comments. Also, the article presents research materials concerning the study of representation in the collection materials, necessary information about the French physician, the author of the method of auscultation, René Laenneck. He is described through philatelic materials, as well as through commemorative and table medals. The text is richly illustrated with illustrative material, explanatory descriptions and comments

Towards the design of nature-inspired materials: Impact of complex pore morphologies via higher-order homogenization

Even though the development of novel materials that mimic nature is widely used in a variety of engineering and scientific fields, the relationship between effective material properties and underlying, often complex pore morphology is still not fully understood. To address this knowledge gap and accelerate the development of novel nature-inspired materials, this paper adopts a higher-order asymptotic homogenization method to numerically investigate the effect of complex micropore morphology on the effective mechanical properties of a porous system. Specifically, we create unique pore morphologies with varying levels of complexity that serve as a more realistic representation of natural materials. We then use the second-order homogenization method to capture the role of pore size, shape, orientation, and distribution on effective properties. By creating different pore morphologies, we systematically studied the relationship between morphology and effective mechanical properties. The results highlight the necessity of higher-order parameters to fully capture the role of realistic pore morphologies on effective mechanical properties and provide a path forward in the design of nature-inspired materials.

Profile of Mathematical Representation Ability to Solve Mechanical Wave Matter Physics Problems

Physical Science develops through a series of scientific processes and cannot be understood by memorization, but rather requires understanding concepts so physicists use various forms of representation. The mathematical representation approach is often used in physics learning. This study aims to examine more deeply the ability of mathematical representation of students of SMA Negeri 1 Cluring class XI MIPA in solving mechanical wave problems. The research method used is descriptive quantitative. The data collection technique used is a written test consisting of five description questions based on mathematical representations of mechanical wave material, interviews, observations, and documentation. The results of the study obtained the average mathematical representation ability of students in solving mechanical wave matter physics problems thanks to the "excellent" category of 68.89%, the "good" category of 9.29%, the "good enough" category of 8.69%, the "not good" category of 1.90%, and 11.24% of students categorized as fail in solving physics problems using mathematical representations of mechanical wave matter.

Interpretation of Phraseological Units Using Chinese Language in Russian-Chinese Phraseological Dictionaries

The description of Russian phraseological units by means of the Chinese language in lexicographic practice is considered. Dictionary articles, their structure and content from six main phraseological dictionaries published in China from the second half of the last century to the present day are analyzed. The results of the research showed that the explanation of Russian phraseology in these lexicographic sources is carried out in two ways: through interpretation and selection of a similar expression. It is shown that the second method is implemented in two variants: as interpretation-translation and literal translation. It has been established that interpretation-translation differs from actual interpretation and literal translation by the presence of two equivalent functions: explanation and interpretation. The compound description model «analogous expression + additional interpretation», which is most often used in dictionary articles where there is no proper interpretation and etymological information is also revealed. On the contrary, when they are present in the dictionary article, a Chinese analogous expression is usually used as a variant of the explanation of the Russian phraseological unit. The authors come to the conclusion that the volume of dictionary articles and models of description of Russian phraseology depend on the goals of dictionary compilers: a brief reference presentation of phraseology, a detailed explanation through an extensive etymological commentary, representation of materials for learning by demonstrating examples, etc.

Evaluation of the effectiveness of neighbors’ selection algorithms in the random cellular automata model of unconstrained grain growth

ABSTRACT Full-field simulations used to enhance the predictive capability of classical finite element models require the preparation of high-quality initial digital material representation (DMR) data. Depending on the selected method, such a procedure can be time-consuming and, in subsequent model runs, result in a significant increase in computational time. An unconstrained grain growth algorithm based on the random cellular automata (RCA) method can be used as a potential solution to provide high-quality DMR. However, the most time-consuming part of that approach, the neighbor-search algorithm, should be optimized from an algorithmic point of view before practical application in full-field analysis. Therefore, four neighbor-search algorithms dedicated to the RCA method were developed and benchmarked within the paper for the algorithm's complexity and impact of individual parameters on computational efficiency. The research has shown that significantly reducing RCA simulation time is possible with the properly developed neighbor-search algorithm and problem-specific code optimizations.

Visualizing Freedom

This essay is the first study of the formerly enslaved woman Ellen Craft’s nineteenth-century photograph album and its contents, and the first study linking Craft’s album to one owned by another nineteenth-century African American woman, Arabella Chapman. Examining Craft’s album and its connections offers an opportunity to consider how African American women employed domestic photograph albums to record their experience of freedom. Past scholarship on photographs of famous formerly enslaved men and women has emphasized how these “self-made” individuals used photography to assert their autonomy in freedom. And, Craft herself participated in this convention when posing for her famous portrait disguised as the White man “William Johnson,” whose identity she passed through in order to obtain her freedom. Yet, this scholarship has not yet engaged the issue of family in photographs of the famous formerly enslaved. My study introduces Craft’s album as visualizing freedom differently through the linkage of photographs of family and friends across pages and under an album’s tight bindings. The book joins photographs together as material representations of Black familial and community bonds that had faced ever-present threats of violation and rupture under slavery. It visualizes freedom not so much in autonomous identity, but via cultivated connections between family and friends in respectable domestic space. Today, the mnemonic notations, fingerprints and smudges on its pages from the hands of four generations of female descendants after Craft reveal the album’s long legacy of preserving affirmative, embodied memories of attachment amongst African American women.