85 Results Found

In the design and development of novel materials that have excellent mechanical properties, classification and regression methods have been diversely used across mechanical deformation simulations or experiments. The use of materials informatics meth...

The application of ceramic particle-reinforced metal matrix composites (CPRMMCs) in the nuclear power sector is primarily dependent on their mechanical and thermal properties. A comprehensive understanding of the structure–property (SP) linkage...

The integration of artificial intelligence (AI) algorithms in materials design is revolutionizing the field of materials engineering thanks to their power to predict material properties, design de novo materials with enhanced features, and discover n...

(1) Background: Properties and descriptors are two forms of molecular in silico representations. Properties can be further divided into functional, e.g., catalyst or drug activity, and material, e.g., X-ray crystal data. Millions of real measured fun...

Computational prediction of crystal materials properties can help to do large-scale in-silicon screening. Recent studies of material informatics have focused on expert design of multi-dimensional interpretable material descriptors/features. However,...

This paper highlights the capability of materials informatics to recreate “property phase diagrams” from an elemental level using electronic and crystal structure properties. A judicious selection of existing data mining techniques, such as Principal...

Perovskite materials have attracted much attention in recent years due to their high performance, especially in the field of photovoltaics. However, the dark side of these materials is their poor stability, which poses a huge challenge to their pract...

Feed-forward neural networks (NNs) are widely used for the machine learning of properties of materials and molecules from descriptors of their composition and structure (materials informatics) as well as in other physics and chemistry applications. O...

A machine learning strategy is presented for the rapid discovery of new polymeric materials satisfying multiple desirable properties. Of particular interest is the design of high refractive index polymers. Our in silico approach employs a series of q...

The separation of high-purity hydrogen from mixed gasses using dense metallic alloy membranes is essential for advancing a hydrogen-based economy. Palladium-based membranes exhibit outstanding catalytic activity and theoretically infinite hydrogen se...

This review is focused on FC-BENTEN, an advanced synchrotron X-ray experimental database developed at SPring-8 with support from Japan’s New Energy and Industrial Technology Development Organization (NEDO). Designed to advance polymer electroly...

The purpose of this paper is to use an informatics-based analysis to develop a rational design approach to the accelerated screening of nano-composite materials. Using existing nano-composite data, we develop a quantitative structure–activity r...

Given the growth of scientific literature on the web, particularly material science, acquiring data precisely from the literature has become more significant. Material information systems, or chemical information systems, play an essential role in di...

Machine learning (ML) has become a transformative tool in nanomaterial research, driven by the rapid growth of data-intensive experimental techniques, multiscale simulations, and computational modeling. This study provides a bibliometric analysis to...

The electrical resistivity and the Hall effect of topological insulator Bi2Te3 and Bi2Se3 single crystals were studied in the temperature range from 4.2 to 300 K and in magnetic fields up to 10 T. Theoretical calculations of the electronic structure...

The microstructure–property relationship is critical for parts made using the emerging additive manufacturing process where highly localized cooling rates bestow spatially varying microstructures in the material. Typically, large temperature gradient...

Machine learning methods allow the prediction of material properties, potentially using only the elemental composition of a molecule or compound, without the knowledge of molecular or crystalline structures. Herein, a composition-based machine learni...

Although measurement data from the civil engineering sector are an important basis for scientific analyses in the field of non-destructive testing (NDT), there is still no uniform representation of these data. An analysis of data sets across differen...

Prof. George Whitesides’ pioneering contributions to the field of self-assembled monolayers (SAMs) have profoundly influenced biointerface science and beyond. This review explores the development of SAMs as highly organized molecular structures...

The rational design of photoanode materials is pivotal for advancing photoelectrochemical (PEC) water splitting toward sustainable hydrogen production. This review highlights recent progress in the machine learning (ML)-assisted development of nanost...

Materials processing is challenging because the final structure and properties often depend on the process conditions as well as the composition. Past research reported in the archival literature provides a valuable source of information for designin...

In this paper, a hybrid neural network (HNN) that combines a convolutional neural network (CNN) and long short-term memory neural network (LSTM) is proposed to extract the high-level characteristics of materials for critical temperature (Tc) predicti...

A novel framework for inverse quantitative structure–activity relationships (inverse QSAR) has recently been proposed and developed using both artificial neural networks and mixed integer linear programming. However, classes of chemical graphs treate...

Dielectric crystals with switchable electric polarizations represent the key functional materials utilized for a broad range of practical applications. They allow for academically intriguing platforms, where the use of a strong external electric fiel...

Accurately predicting glass density is crucial for designing novel materials. This study aims to develop a robust predictive model for the density of oxide glasses and, more importantly, to investigate how physically informed feature engineering can...

This study presents a robust machine learning framework based on Gaussian process regression (GPR) to predict the tensile strength of polymer nanocomposites reinforced with various nanofillers and processed under diverse techniques. A comprehensive d...

The intrinsic structural stability of ABO3 perovskite materials is a pivotal factor determining their efficiency and durability in photovoltaic applications. However, accurately predicting stability, commonly measured by the energy above hull metric,...

Knowledge graphs (KGs) offer a structured and collaborative approach to integrating diverse knowledge from various domains. However, constructing knowledge graphs typically requires significant manual effort and heavily relies on pretrained models, l...

Optimizing heat treatment processes requires an understanding of the complex relationships between compositions, processing parameters, microstructures, and properties. Traditional experimental approaches are costly and time-consuming, whereas machin...

China’s modernization endeavors to advance both material and spiritual civilization concomitantly; nevertheless, the nation presently faces a pressing issue of urban pollution. This research examines how the introduction of information technolo...

The authors of this article advocate the view that the transformation from a physics paradigm to an informatics paradigm is not a denial of the effectiveness and scope of application of the physics paradigm, but a criticism of the “arrogance&rd...

This paper discusses the “what”, “why”, and “how” of the issue of “discipline informatization”. Since the Renaissance, the humanities and social sciences established under the guidance of physical-mathe...

This paper focuses on the interdisciplinary research between Theoretical Informatics (TI) and the Humanities (philosophy, history, literature, etc.). There are five main sections: 1. A brief introduction to TI and its functions in the aspects of worl...

The development of novel technologies and applications for micro/nano devices is an interdisciplinary subject that demands an integration of several research fields, such as material science, biotechnology, medicine, chemistry, informatics, optics, e...

The development of information technology has transformed various aspects of life, including education, by making it more flexible, interactive, and accessible. One platform that plays an important role in this transformation is YouTube, a video shar...

The rapid dispersion of new psychoactive substances (NPS) presents challenges to customs services and analytical laboratories, which are involved in their detection and characterization. When the seized material is limited in quantity or of a complex...

Background and objectives: Loeys–Dietz syndrome 3, also known as aneurysms-–osteoarthritis syndrome, is an autosomal dominant genetic connective tissue disease caused by pathogenic variants in SMAD3, a transcription factor involved in TGF...

A data cooperative is a legally organized cooperative whose purpose is the collective collection, management, and use of data collected by its members. In contrast to other data pools, it can act as a democratic self-governing organization in which e...

Driven by industry–education integration and digital technology, higher vocational education textbooks are transitioning from traditional formats to an integrated “loose-leaf + digital” model. Combining the flexibility of loose-leaf...

An aging population, the effects of pandemics and civilization-related conditions, and limited leapfrogging in the number of rehabilitation and physiotherapy specialists are driving demand for modern assistive technologies, especially upper and lower...

Nowadays, density functional theory (DFT)-based high-throughput computational approach is becoming more efficient and, thus, attractive for finding advanced materials for electrochemical applications. In this work, we illustrate how theoretical model...

Adding acid-modified resin compatibilizers is essential for plastic composites reinforced with carbon-neutral cellulosic filler. Researchers have measured the efficacy of adding a compatibilizer in the context of mechanics. However, it is necessary t...

This paper argues that generative music and art techniques have much to tell us about the politics of operations in infrastructural and political contexts, that generative techniques are an unavoidable aspect of informatic operations, and that an art...

The development of photo-/electro-chemical and flexible electronics has stimulated research in catalysis, informatics, biomedicine, energy conversion, and storage applications. Gels (e.g., aerogel, hydrogel) comprise a range of polymers with three-di...

The synergy between dentistry and informatics has allowed the emergence of new technologies, specifically 3D printing, which has led to the development of new materials. The aim of this research was to compare the mechanical properties of dental base...

Nowadays, society is oriented toward reducing the production of plastics, which have a significant impact on the environment. In this context, the recycling of existing plastic objects is currently a fundamental step in the mitigation of pollution. V...

The rapid development of the process of informatization of modern society has necessitated cybersecurity in all spheres of human activity, as the implementation of deliberate or unintentional influences on the information sphere by both external and...

The authors of this article believe that from the perspective of the development of human spiritual products, the transformation of our scientific worldview and methodology from the paradigm of physics to the paradigm of informatics is an inevitable...

With the rapid advancement of artificial intelligence and machine learning technologies, the demand for enhanced device computing capabilities has significantly increased. Neuromorphic computing, an emerging computational paradigm inspired by the hum...

By the onset of the 21st century, online presence had become prevalent in higher education, with the COVID-19 pandemic further accelerating this trend. However, the success of online education and its assessment by students can differ significantly f...