The integration of explainable artificial intelligence (XAI) with computational materials science has emerged as a transformative paradigm for accelerating materials discovery and understanding structure– property relationships. This investigation executes an exhaustive bibliometric evaluation to map the scholarly convergence of computational materials science and explainable artificial intelligence (XAI). Using a systematic search in the Web of Science Core Collection, 157 relevant articles published between 2019 and 2026 were identified. VOSviewer software was employed to map co-authorship networks (authors, organizations, countries), keyword co-occurrence, and citation networks (documents, sources, authors). The results reveal a rapidly growing field, with annual publications rising from 2 in 2019 to 34 in 2026. Co-authorship analysis shows a dominant cluster of researchers centered around CALPHAD-based alloy design, and a distinct international collaboration network led by the USA, Germany, and South Korea. Keyword co-occurrence highlights four thematic clusters: (i) explainable AI and interpretable machine learning, (ii) material informatics and design, (iii) SHAP and feature selection, and (iv) specific techniques such as deep learning and friction stir welding. Citation analysis indicates that the most influential works focus on high-strength aluminum alloy design and CALPHAD-driven inverse predictions. The sparse citation linkages among documents and sources suggest a fragmented early-stage field. This bibliometric overview provides a foundation for researchers to identify emerging trends, influential contributors, and thematic gaps, thereby guiding future interdisciplinary efforts in explainable computational materials science.

The utilization of composite materials has significantly surged in the aeronautics, automotive, military, and civil construction sectors, replacing conventional materials like steel, aluminum, and other alloys. Laminated composite materials possess elevated modulus-to-weight and strength-to-weight ratios, along with superior fatigue resistance and non-corrosive characteristics. This study aims to examine the influence of manual lay-up, vacuum bagging, and vacuum assisted resin transfer molding methods on the tensile and flexural properties of glass/Epoxy composites. The tensile and flexural test specimen was fabricated and evaluated in accordance with ASTM D3039 and ASTM D7264 standards, respectively. The samples were evaluated using a computer-controlled electro-hydraulic universal testing machine. The specimen was analyzed using a Scanning Electron Microscope (SEM) to assess the laminate's uniformity. The maximum tensile and flexural strength recorded for hand layup approach is 280 MPa and 320 MPa, respectively. Nevertheless, the vacuum bagging method yielded a better result than hand lay-up techniques. The results achieved during those tests were 330 MPa and 435 MPa, respectively. In contrast, the VARTM provides a superior tensile and flexural properties, i.e. 444.49 MPa and 560 MPa, respectively. The samples produced using the VARTM technique exhibit superior mechanical properties, demonstrating differences of 17.86\%, 32.14\%, and 35.94\% in tensile, shear, and bending tests, respectively, compared to those made using hand layup. Consequently, the mechanical properties of the laminate were enhanced by vacuum bagging procedures compared to hand lay-up approaches.

The rheological characterization of polymer solutions is critical for optimizing their performance in various industrial applications, including enhanced oil recovery, food processing, and biomedical engineering. Traditional experimental methods for classifying flow behavior, viscosity, shear-thinning degree, and system stability are often time-consuming and require expert interpretation. This study presents a data-driven approach to automate these classifications using a Gaussian Naive Bayes (GNB) classifier. A comprehensive dataset comprising 420 experimental measurements of polymer solutions, sourced from Kaggle, was used. The dataset includes key physicochemical features: shear rate, polymer concentration, NaCl concentration, Ca²⁺ concentration, temperature, and logarithmic viscosity. These features were used to predict four distinct target labels: Newtonian vs. Non-Newtonian flow behavior, Shear Thinning Degree, Viscosity Class, and System Stability. The GNB model, chosen for its suitability with continuous features and probabilistic foundation, was trained and evaluated using an 80/20 train-test split and 5-fold cross-validation. The model achieved high test accuracies of 0.857 (Flow Behavior), 0.798 (Shear Thinning Degree), 1.000 (System Stability), and 0.940 (Viscosity Class). Cross-validation scores confirmed model robustness. Permutation feature importance analysis revealed that the slope of the log(viscosity) vs. log(shear rate) relationship, temperature, and Ca²⁺ concentration were the most influential features across all classification tasks. This study demonstrates the efficacy of simple yet interpretable machine learning models in accurately predicting complex rheological properties from basic formulation parameters, paving the way for rapid, low-cost material screening and process optimization.

The escalating global demand for rare earth elements (REEs) in green energy and advanced technologies has made efficient resource assessment more critical than ever. However, conventional laboratory characterization methods in geological samples, such as inductively coupled plasma mass spectrometry and x-ray fluorescence, are often destructive, time-consuming, and cost-intensive. Consequently, to accelerate exploration and processing pipelines, there is an urgent need to transition toward rapid, in-situ, and non-destructive analytical techniques for mineralogical screening. This review explores the paradigm shift brought about by synergizing modern spectroscopic methods and microchemical imaging with advanced machine learning (ML) and data-driven architectures. Spectroscopic methods evaluated include visible to shortwave infrared spectroscopy, laser-induced breakdown spectroscopy, and mass spectrometry. Microchemical imaging focuses on scanning electron microscopy coupled with energy dispersive x-ray spectroscopy to map complex mineral phases. Traditional mineralogical and spectral analyses of complex geological ores and secondary resources (e.g., coal fly ash, e-waste, and bauxites) are frequently hindered by severe matrix effects, elemental interferences, and dense spectral overlaps. Here, it is highlighted how intelligent algorithmic models effectively decode these high-dimensional mineralogical datasets. Rather than focusing on pure computational architecture, this paper emphasizes how AI-driven frameworks autonomously extract hidden geochemical correlations, suppress geological background noise, and identify multi-elemental patterns or microstructural mineral inclusions. Ultimately, the deployment of ML-empowered optical sensors and automated mineral tracker algorithms significantly improves the detection limits, phase quantification, and prediction accuracy for REEs. This integration establishes a robust foundation for autonomous mineral exploration, efficient metallurgical resource screening, and the advancement of processing technologies within the circular economy.

This study presents a computational investigation into the effects of tin (Sn) content variation on the phase stability, phase transformation behavior, and transformation kinetics of Zr-Sn-Cr ternary alloys. Using the JMatPro thermokinetic simulation software, phase fraction diagrams and time-temperature-transformation (TTT) diagrams were constructed for three alloy compositions: Zr-0.1Cr-1.5Sn, Zr-0.1Cr-3.0Sn, and Zr-0.1Cr-6.0Sn (all in wt.%), with chromium content held constant at 0.1 wt.%. The results demonstrate that increasing Sn content significantly enhances the stability and formation temperature of the Zr₄Sn intermetallic phase while concurrently reducing the alpha phase fraction. The C15_FCC (ZrCr₂) Laves phase exhibited minimal sensitivity to Sn addition in terms of phase fraction, though its onset formation temperature increased progressively. Furthermore, Sn enrichment was found to elevate the alpha-beta transformation temperatures while narrowing the beta phase stability range, indicating reduced beta phase stability. TTT diagram analysis revealed accelerated alpha phase precipitation kinetics with increasing Sn content, as evidenced by shorter precipitation times and higher formation temperatures. These findings provide valuable insights for the design of Sn-containing zirconium alloys for nuclear cladding and other high-temperature applications.