Materials Science Approach Research Articles

High-Accuracy Prediction of Mechanical Properties of Ni-Cr-Fe Alloys Using Machine Learning

Artificial intelligence-driven prediction models have emerged as powerful tools for estimating material properties with high accuracy, yet the preparation of training datasets often demands labor-intensive and time-consuming experimental procedures. Leveraging the Computational Materials Science (CMS) approach, this study utilizes phase transformation calculations and thermodynamic data to simulate the mechanical properties of Ni-Cr-Fe alloys. Using JMatPro software, mechanical properties (0.2% Proof Stress, Fracture Stress, and Young's Modulus) of 50 Ni-Cr-Fe alloy compositions were simulated across a temperature range of 540–920°C, generating a dataset of 1000 rows. This dataset was used to train an Artificial Neural Network (ANN) model, with 80% allocated for training and 20% for validation and testing. The trained AI model demonstrated robust predictive capabilities, achieving a 96,61% accuracy rate in forecasting material compositions with the desired thermo-physical properties at specific temperatures. To validate the model's reliability, predicted alloy compositions were re-simulated under identical conditions in JMatPro, confirming the high fidelity of the model's predictions. The results underscore the efficacy of Computational Materials Science (CMS)-generated datasets as a scalable and reliable source for training AI models in materials science. This study highlights the potential of integrating Computational Materials Science (CMS) and Machine Learning approaches to accelerate material design and development processes, delivering significant improvements in prediction speed and accuracy.

Investigation of the Effect of Alloying Elements on the Density of Titanium-Based Biomedical Materials Using Explainable Artificial Intelligence

Titanium alloys are widely preferred in the healthcare sector as biocompatible materials due to their superior properties such as low density and exceptional mechanical strength. Their low density provides lightweight solutions, and their density is closer to that of human bone compared to other metallic alloys with similar strength. This similarity facilitates a balanced load distribution between the bone and the implant, enhancing biomechanical compatibility. This study investigates the effects of alloying elements on the density of titanium-based biomedical materials using a computational materials science approach. A total of 72 different compositions of Ti-Al-V alloys were modeled using JMatPro software, and their densities were simulated at room temperature (25°C). The simulation produced a comprehensive dataset, which was utilized to train an explainable artificial intelligence (XAI) model. Advanced interpretability techniques, including SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and Partial Dependence Plots (PDP), were employed to elucidate the influence of each alloying element on the density. The dataset was analyzed using an XAI-based regression model implemented with the Artificial Neural Network (ANN) algorithm. The interpretability graphs provided insights into the individual contributions of the alloying elements, revealing their positive or negative effects on the density. The findings offer a deeper understanding of the role of alloying elements in optimizing the performance of titanium-based biomedical materials, particularly in achieving lightweight designs. This study highlights the potential of integrating computational material modeling with explainable AI to advance the design and development of high-performance lightweight materials for biomedical applications.

Small Data Machine Learning Approaches in Molecular and Materials Science

Small Data Machine Learning Approaches in Molecular and Materials Science

МЕТОДОЛОГИЧЕСКИЕ ОСНОВЫ МАТЕРИАЛОВЕДЧЕСКОЙ ОЦЕНКИ КАЧЕСТВА СМАЗОЧНЫХ СРЕД ДЛЯ НАГРУЖЕННЫХ СОПРЯЖЕНИЙ МАШИН И МЕХАНИЗМОВ. Сообщение 2. Влияние состава смазочных сред на структурно-фазовое состояние зоны деформации металлических трибосопряжений и их антифрикционные свойства

The results of experimental studies of deformation and diffusion in the surface layers of metallic frictional couples, which form the experimental and theoretical basis of the materials science approach to the quality evaluation of lubricants, are presented. The concepts of the component’s role for the lubricating medium in the implementation of the plasticizing and strengthening triboeffect are formulated. Structural condition characteristics for the surface layers of copper alloys during friction on steel in the mode of boundary lubrication in surface-active lubricants, are described. The conditions for achieving minimum power losses of such tribosystems for friction and wear have been found. The greatest service life of tribounit in a lubricating medium containing surfactants is resulted from wear resistance increase of a copper alloy with a relative decrease in hardness and increased plasticity of its surface layer. At the same time, a decrease in hardness due to the plasticizing effect of surfactants occurs only in the near-surface deformed layer of tribomaterial, while in its deeper layers with no plasticization resulted from a lubricating medium effect, the mechanical characteristics of the antifriction alloy remain at the required high level. It is demonstrated that alloys with a single-phase structure of an α-solid solution and a wide concentration range of solubility of the alloying element in the solid state meet these requirements. It is found that such boundary conditions are provided by the presence of stationary macroscopic diffusion flows of alloying elements in the zone of contact deformation of a polycomponent tribomaterial. The role of local diffusion phenomena in quasispinodal phase transitions is indicated. Examples of the selective transfer phenomenon during friction in industrial lubricants, are given.

Concepts for a Semantically Accessible Materials Data Space: Overview over Specific Implementations in Materials Science

This article describes advancements in the ongoing digital transformation in materials science and engineering. It is driven by domain‐specific successes and the development of specialized digital data spaces. There is an evident and increasing need for standardization across various subdomains to support science data exchange across entities. The MaterialDigital Initiative, funded by the German Federal Ministry of Education and Research, takes on a key role in this context, fostering collaborative efforts to establish a unified materials data space. The implementation of digital workflows and Semantic Web technologies, such as ontologies and knowledge graphs, facilitates the semantic integration of heterogeneous data and tools at multiple scales. Central to this effort is the prototyping of a knowledge graph that employs application ontologies tailored to specific data domains, thereby enhancing semantic interoperability. The collaborative approach of the Initiative's community provides significant support infrastructure for understanding and implementing standardized data structures, enhancing the efficiency of data‐driven processes in materials development and discovery. Insights and methodologies developed via the MaterialDigital Initiative emphasize the transformative potential of ontology‐based approaches in materials science, paving the way toward simplified integration into a unified, consolidated data space of high value.

Surface Plasmon-Driven Versatile Enhancement of Chemosensing.

Chemo-sensors have deeply integrated into various facets of our daily lives. To further satisfy the increasing performance demand, the current attempts are mainly centered on materials science approaches, usually involving time-& labor-consuming structure designing, synthesis, and modification. To date, it remains largely unexplored to enhance sensing material performance at the fundamental physical level by strategic exploitation of optical properties. In this work, we proposed a facile and versatile approach for improving the material performance by strategically utilizing the surface plasmon resonance─a characteristic property of optical devices. This approach is revealed to have a dual effect on fluorescence-based chemosensing: it amplifies the collection of fluorescence signals and simultaneously expedites the kinetics of chemical reactions. In this work, we developed a surface plasmon-driven fluorescence-based chemosensor that utilizes the 2,4,6-trisformyl phenol-diethylamine (TFP-I) fluorescent probe for the detection of hydrogen peroxide (H2O2) gas molecules. By harnessing the dual-effect induced by surface plasmons, we achieved outstanding sensing performance for H2O2 gas molecules, characterized by 0.0225 ppt sensitivity and an exceedingly low limit of detection. This study substantiates the applicability of the surface plasmon resonance-based optical effect in the realm of fluorescent chemical materials for sensing performance amplification. Beyond this, it pioneers the strategic harnessing of optical effects to manipulate the performance of chemical materials, particularly for the advancement of sensing capabilities.

A Material Type Prediction Model Based on Machine Learning Technology

In materials science, traditional experimental and computational approaches require the investment of enormous amounts of time and resources, and the experimental conditions limit the use of these methods. Sometimes, traditional approaches may not yield satisfactory results for the desired purpose. Therefore, it is essential to develop a new approach to accelerate experimental progress and avoid unnecessary waste of time and resources.

Choosing between additive and conventional manufacturing of spare parts: On the impact of failure rate uncertainties and the tools to reduce them

Choosing between additive and conventional manufacturing of spare parts: On the impact of failure rate uncertainties and the tools to reduce them

Fabrication and Fluorescence Analysis of Rhodamine Dyes in Polycarbonate Serpentine Microfluidic System.

The synthesis of rhodamine dyes (R6G and R1010) and their fluorescence characterization within polymer-based microfluidics, offers an exciting and novel approach in materials science and chemical analysis. This work investigates the emission of polycarbonate substrates (PC) by UV-visible. The ablation threshold (16mj.sec-1) of PC at 193nm wavelength after that ablation process continued to produce microfluidic serpentine channels on PC by using G-Code. The fluorescence characteristics of Rhodamine 6G and Rhodamine 101 are investigated. Absorption and emission at peak wavelength were analyzed against R6G and R101 concentrations. Furthermore, the refractive indices of both R6G and R101 vis concentrations are examined. As a result at low concentrations, there was the highest overlapping, and at high concentrations, there was the smallest overlapping. R101 showed better photostability and a more consistent diffusion, whereas R6G had a faster diffusion and stronger fluorescence intensity. These differences were caused by the different molecular structures of the dyes and their interactions with the PCmicrochannel. Incorporating R6G and R101 dyes into a polycarbonate PC microfluidic chip would enhances both the resolution and sensitivity of fluorescence detection. The limited microfluidic setup facilitates ultra-high-resolution investigation and minimizing sample volumes, making it suitable for applications requiring precise measurements. The innovation relies on the utilization of the unique fluorescence characteristics of R6G (Rhodamine 6G) and R101 (Rhodamine 101) dyes to enhance the performance of polycarbonate microfluidic devices. R6G has high fluorescence quantum yield and stability, rendering it suitable for sensitive detection, while R101 offers superior brightness and improved resistance to photobleaching. Incorporation of these dyes into polymeric microfluidics improves sensitivity and facilitates real-time, dynamic sample analysis. This method offers a portable, economical solution with high-throughput capabilities, greatly enhancing both analytical and process accuracy across a variety of applications.

Heteroatom Immobilization Engineering toward High-Performance Metal Anodes.

Heteroatom immobilization engineering (HAIE) is becoming a forefront approach in materials science and engineering, focusing on the precise control and manipulation of atomic-level interactions within heterogeneous systems. HAIE has emerged as an efficient strategy to fabricate single-atom sites for enhancing the performance of metal-based batteries. Despite the significant progress achieved through HAIE in metal anodes for metal-based batteries, several critical challenges such as metal dendrites, side reactions, and sluggish reaction kinetics are still present. In this review, we delve into the fundamental principles underlying heteroatom immobilization engineering in metal anodes, aiming to elucidate its role in enhancing the electrochemical performance in batteries. We systematically investigate how HAIE facilitates uniform nucleation of metal in anodes, how HAIE inhibits side reactions at the metal anode-electrolyte interface, and the role of HAIE in promoting the desolvation of metal ions and accelerating reaction kinetics within metal-based batteries. Finally, we discuss various strategies for implementing HAIE in electrode materials, such as high-temperature pyrolysis, vacancy reduction, and molten-salt etching and anchoring. These strategies include selecting appropriate heteroatoms, optimizing immobilization methods, and constructing material architectures. They can be utilized to further refine the performance to enhance the capabilities of HAIE and facilitate its widespread application in next-generation metal-based battery technologies.

Direct Calculations of the Dielectric and Optical Parameters of Some Compound Semiconductors: A Study Using the Lorentz Oscillator and Wemple-DiDomenico Models

The Lorentz single oscillator model is widely utilized to describe the electron-phonon interaction in solids. The use of this model to calculate the dielectric function and optical parameters of compound semiconductors represents a novel approach in materials science which may lead to develop new materials with tailored optical properties and consequently can offer vast applications in areas such as quantum computing and quantum information processing. This work presents direct calculations of the complex dielectric function and some optical parameters, such as refractive index, extinction coefficient, and reflectivity for some selected III-V and II-VI compound semiconductors using Lorentz oscillator model. The dielectric and optical dispersion parameters among the infrared and visible electromagnetic spectra follows the so-called Transmission-Absorption-Reflection-Transmission (TART) trend. The TART curve provides information about how a material interacts with light at different wavelengths, which is crucial for understanding and optimizing various optoelectronic devices. Moreover, dielectric loss functions such as loss tangent, surface energy loss function and volume energy loss function are investigated. The refractive index dispersion is analyzed using Wemple–DiDomenico single effective oscillator model and Sellmeier equation. The energy of the effective single oscillator (Eo) and the dispersion energy (Ed) are estimated. Received: 10 May 2024 | Revised: 19 June 2024 | Accepted: 9 August 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement S. Abboudy: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. K. Alfaramawi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. L. Abulnasr: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Basma K. Hefny: Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft.

AIPHAD, an active learning web application for visual understanding of phase diagrams

Phase diagrams provide considerable information that is vital for materials exploration. However, the determination of multidimensional phase diagrams typically requires a significant investment of time, cost, and human resources owing to the necessity of numerous experiments or simulations. Machine learning and artificial intelligence techniques present a viable solution for expediting phase diagrams investigations. Additionally, effective visualization is critical for understanding phase diagrams. This study reports the development of AIPHAD (Artificial Intelligence technique for PHAse Diagram), an open-source web application to assist in the investigation and visual understanding of phase diagrams using active learning. AIPHAD employs PDC (Phase Diagram Construction) algorithm, which operates on the principle of uncertainty sampling in active learning. The AIPHAD application facilitates the examination of five diagram types: two-variable diagrams, three-variable diagrams, ternary sections, ternary phase diagrams, and quaternary sections. The efficacy of the application is demonstrated in the study of the Fe-Ti-Sn ternary system, where it efficiently identified the presence of the Heusler phase. The integration of machine learning tools with traditional materials science approaches showcased in this study has the potential to drive groundbreaking advancements in materials exploration and discovery.

Higher‐Order Behaviours in Bio‐Inspired Materials

AbstractBio‐inspired approaches in materials science and systems chemistry are yielding a variety of stimuli‐responsive and dynamic materials that are gradually changing our everyday life. However, the ability to chemically program these materials to exhibit macroscopic higher‐order behaviours such as self‐assembly, contractility, swarming, taxis, chemical communication, or predator‐prey dynamics remains an ongoing challenge. While still in its infancy, the successful fabrication of bio‐inspired materials displaying higher‐order behaviours not only will help bridging the gap between living and non‐living matter, but it will also contribute to the development of advanced materials for potential applications ranging from tissue engineering and biotechnology, to soft robotics and regenerative medicine. Our Mini‐Review will systematically discuss the higher‐order behaviours developed thus far in bio‐inspired systems, namely (i) polymer networks (ii) microbots, (iii) protocells, and (iv) prototissues. For each system it will provide key examples and highlight how the emergent behaviour could be chemically programmed.

Unsaturated polyester resin based composites: A case study of lignin valorisation

Materials from green resources boast a low carbon footprint, forming the foundation of the circular economy approach in materials science. Thus, in this study, waste poly(ethylene terephthalate) (PET) was subjected to depolymerization using propylene glycol (PG), and subsequent polycondensation with bio-based maleic anhydride (MA) produced unsaturated polyester resin (b-UPR). Bio-derived acryloyl-modified Kraft lignin (KfL-A) served as a vinyl reactive filler in the b-UPR matrix to create b-UPR/KfL-A composites. The structural characterization of KfL-A and b-UPR involved the use of FTIR and NMR techniques. The mechanical properties of the newly fabricated composites were assessed through tensile strength, Vickers microhardness, and dynamic mechanical tests. The addition of KfL-A to the rigid b-UPR matrix enhanced material flexibility, resulting in less stiff and hard materials while preserving composite toughness. For instance, incorporating 10wt% of KfL-A in b-UPR led to a 17% reduction in hardness, a 48% decrease in tensile strength, and a 20% reduction in toughness. Positive environmental impact was achieved by incorporation of 64wt% of renewable and recycled raw material. Analogously prepared b-UPR/KfL composites showed structural inhomogeneity and somewhat better mechanical properties. Transmission (TEM) and scanning (SEM) electron microscopies revealed a suitable relationship between mechanical and structural properties of composites in relation to the extent of KfL-A addition. The UL94V flammability rating confirmed that flame resistance increased proportionally with the KfL-A addition. Once deposited in a landfill, these composites are expected to disintegrate more easily than PET, causing less harm to the environment and contributing to sustainability in the plastics cycle.

Solving Intractable Chemical Problems by Tensor Decomposition.

Many complex chemical problems encoded in terms of physics-based models become computationally intractable for traditional numerical approaches due to their unfavorable scaling with increasing molecular size. Tensor decomposition techniques can overcome such challenges by decomposing unattainably large numerical representations of chemical problems into smaller, tractable ones. In the first two decades of this century, algorithms based on such tensor factorizations have become state-of-the-art methods in various branches of computational chemistry, ranging from molecular quantum dynamics to electronic structure theory and machine learning. Here, we consider the role that tensor decomposition schemes have played in expanding the scope of computational chemistry. We relate some of the most prominent methods to their common underlying tensor network formalisms, providing a unified perspective on leading tensor-based approaches in chemistry and materials science.

Mechanical Properties, Tissue Structure, and Elemental Composition of the Walking Leg Tips of Coconut Crabs

The coconut crab, Birgus latro, has black protrusions on the tops of its walking legs and claw fingers. In addition, there are regularly aligned small black protrusions on parts of the exoskeleton surface of the claws and leg. In this study, the elemental composition, crystal structure, tissue structure, and mechanical properties of these protrusions were studied using a materials science approach, and the results were compared with those of mineralized cuticle. These leg tips were found to be a non-calcified fibrous tissue of α-chitin connected to the mineralized cuticle. The tip of the second walking leg was elongated and had a pointed shape with an oval cavity at its center that was more than 1000 times larger than the pore tubes (100–350 nm) of the mineralized cuticle. It was very soft, with a hardness of 0.4 GPa, corresponding to 11–12% of the hardness of the hard exocuticle and 55–57% of the hardness of the soft endocuticle. The elastic modulus of 8.0 GPa obtained by means of nanoindentation testing was consistent with that of α-chitin fibers of shrimp shells obtained by means of tensile testing. These soft protrusions provide a secure grip on the surfaces of trees or rocks and protect the claw fingertips. It was concluded that the black protrusions are related to a unique ecological (engaging in vertical movements, entering and exiting limestone caves, and escape behavior) aspect of the coconut crab, the largest terrestrial crustacean.

Green chemistry approaches in materials science: Physico-mechanical properties and sustainable applications of grass fiber reinforced composites

The pressing environmental challenges have catalyzed a burgeoning interest in creating composite derived from biodegradable resources. Among these, researchers have garnered significant attention from grass family (Poaceae) fibers for their...

Numerical treatment of reactive diffusion using the discontinuous Galerkin method

AbstractThis work presents a new finite element variational formulation for the numerical treatment of diffusional phase transformations using the discontinuous Galerkin method (DGM). Steep concentration and property gradients near phase boundaries require particular focus on a sound numerical treatment. There are different ways to tackle this problem ranging from (i) the well-known phase field method (PFM) (Biner et al. in Programming phase-field modeling, Springer, Berlin, 2017, Emmerich in The diffuse interface approach in materials science: thermodynamic concepts and applications of phase-field models, Springer, Berlin, 2003), where the interface is described continuously to (ii) methods that allow sharp transitions at phase boundaries, such as reactive diffusion models (Svoboda and Fischer in Comput Mater Sci 127:136–140, 2017, 78:39–46, 2013, Svoboda et al. in Comput Mater Sci 95:309–315, 2014). Phase transformation problems with continuous property changes can be implemented using the continuous Galerkin method (GM). Sharp interface models, however, lead to stability problems with the GM. A method that is able to treat the features of sharp interface models is the discontinuous Galerkin method. This method is well understood for regular diffusion problems (Cockburn in ZAMM J Appl Math Mech 83(11):731–754, 2003). As will be shown, it is also particularly well suited to model phase transformations. We discuss the thermodynamic background by review of a multi-phase, binary system. A new DGM formulation for the phase transformation problem with sharp interfaces is then introduced. Finally, the derived method is used in a 2D microstructural evolution simulation that features a binary, three-phase system that also takes the vacancy mechanism of solid body diffusion into account.

OpenMSCG: A Software Tool for Bottom-Up Coarse-Graining.

The "bottom-up" approach to coarse-graining, for building accurate and efficient computational models to simulate large-scale and complex phenomena and processes, is an important approach in computational chemistry, biophysics, and materials science. As one example, the Multiscale Coarse-Graining (MS-CG) approach to developing CG models can be rigorously derived using statistical mechanics applied to fine-grained, i.e., all-atom simulation data for a given system. Under a number of circumstances, a systematic procedure, such as MS-CG modeling, is particularly valuable. Here, we present the development of the OpenMSCG software, a modularized open-source software that provides a collection of successful and widely applied bottom-up CG methods, including Boltzmann Inversion (BI), Force-Matching (FM), Ultra-Coarse-Graining (UCG), Relative Entropy Minimization (REM), Essential Dynamics Coarse-Graining (EDCG), and Heterogeneous Elastic Network Modeling (HeteroENM). OpenMSCG is a high-performance and comprehensive toolset that can be used to derive CG models from large-scale fine-grained simulation data in file formats from common molecular dynamics (MD) software packages, such as GROMACS, LAMMPS, and NAMD. OpenMSCG is modularized in the Python programming framework, which allows users to create and customize modeling "recipes" for reproducible results, thus greatly improving the reliability, reproducibility, and sharing of bottom-up CG models and their applications.

Digital Characteristics of Microstructure of Diamond—Silicon Carbide Composites

As an example of the implementation of digital materials science approaches based on statistical processing of electron micrographs with the analysis of fractal parameters, the digital characteristics of microstructure of diamond–silicon carbide ceramic composite material are calculated. The lacunarity parameter characterizing the non-uniform distribution of filler particles in the matrix is found. Based on lacunarity values calculated at different scales, scale invariance parameter characterizing the dependence of lacunarity on the scale is evaluated. Voronoi entropy characterizing the structure based on the quantity of information is also calculated and used to determine the average number of neighboring particles and average distance between them. For the composites with high mechanical properties, the number of nearest neighbors approaches six, indicating an almost closest packing.