2024· International Journal of Modern Research in Science & Engineering· 0 citations
TL;DR
The proposed framework combines data preprocessing, feature engineering, predictive modeling, optimization, and sustainability assessment to identify materials that satisfy both engineering and environmental requirements.
Abstract
The increasing demand for sustainable, high-performance materials has accelerated the adoption of Artificial Intelligence (AI) in materials discovery. Traditional material development relies on time-consuming experiments and computationally intensive simulations, limiting scalability and innovation. AI-driven materials discovery integrates machine learning, deep learning, data analytics, and computational materials science to rapidly predict, optimize, and design advanced materials with improved mechanical, thermal, electrical, and environmental performance. The proposed framework combines data preprocessing, feature engineering, predictive modeling, optimization, and sustainability assessment to identify materials that satisfy both engineering and environmental requirements. Advanced algorithms, including Random Forest, Support Vector Machines, Neural Networks, and Gradient Boosting, accurately predict material properties, while generative AI enables inverse design of novel, recyclable, and energy-efficient materials. Sustainability metrics such as life-cycle assessment and carbon footprint guide multi-objective optimization. Despite challenges in data quality and validation, emerging technologies including federated learning, physics-informed neural networks, digital twins, and autonomous laboratories are expected to further advance AI-enabled sustainable materials discovery for Industry 5.0 and resource-efficient engineering.
This review examines how AI methodologies, ranging from machine learning‐assisted first‐principles simulations to deep‐learning analysis of experimental data, are reshaping the study of HfO‐based ferroelectrics to enable predictive design and autonomous optimization of next‐generation hafnia‐based ferroelectrics.
Faizan Ali, D. Lehninger, F. Sánchez et al.· Advanced Electronic Material...· 0 citations
Artificial intelligence (AI) and nanotechnology have emerged as two transformative scientific domains whose convergence is accelerating the development of next-generation nanomaterials with enhanced functionality, precision, and sustainability. AI-driven computational intelligence enables rapid material discovery, predictive modelling, autonomous experimentation, and process optimisation, thereby significantly reducing the time, cost, and complexity associated with conventional nanomaterial research. Simultaneously, advances in nanotechnology have expanded the possibilities for engineering materials with exceptional electrical, optical, mechanical, catalytic, and biomedical properties. The integration of machine learning, deep learning, computer vision, and data-driven optimisation with nanoscale material design has opened new avenues for applications in healthcare, energy storage, environmental remediation, electronics, aerospace, and smart manufacturing. Furthermore, intelligent digital platforms facilitate real-time quality assessment, defect prediction, and performance optimisation across the nanomaterial life cycle. Despite remarkable progress, challenges related to data quality, model interpretability, scalability, standardisation, and ethical deployment remain significant barriers to widespread industrial implementation. This paper presents a comprehensive academic investigation into the synergistic relationship between AI and nanotechnology, highlighting recent innovations, emerging methodologies, application domains, current limitations, and future research opportunities that are expected to shape intelligent nanomaterial development for sustainable scientific and industrial advancement.
Amjid Nadeem, Brajesh Kumar Mishra, J. Raja et al.· International journal of com...· 0 citations
The discovery of advanced alloys capable of withstanding extreme environmental conditions such as high temperatures, intense radiation, corrosive atmospheres, and mechanical stress is critical for applications in aerospace, nuclear energy, deep-sea exploration, and space missions. Traditional experimental and computational approaches to alloy design are often time-consuming and resource-intensive. Recent advances in artificial intelligence (AI) and machine learning (ML) offer powerful tools to accelerate the discovery process by enabling high-throughput screening, property prediction, and design optimization. This paper presents a comprehensive review and methodology for AI-assisted alloy discovery, focusing on the integration of data-driven models with physical principles, high-fidelity simulations, and experimental validation. We highlight successful case studies, discuss the challenges of data scarcity and model interpretability, and propose a framework for closed-loop design that incorporates generative models and active learning. This AI-driven approach represents a paradigm shift toward faster, more cost-effective discovery of next-generation materials for extreme environments.
Venkatesh Iyer, Nandhini Ravi· International Journal of Mod...· 0 citations
The intrinsic challenges of accessing historical dark data in materials science are described and contrasted with timely opportunities for leveraging massive amounts of experimental data from laboratories in going forwards; by exploiting electronic-lab notebooks, high-throughput experiments, and digital-twin technologies.
Jacqueline M. Cole· Advances in Materials· 0 citations
Accurate prediction and optimization of polymer composite properties is of paramount importance in the design of these lightweight, durable, and sustainable materials within renewable energy technologies. This work will provide a holistic machine learning-assisted framework that unites materials informatics with domain-specific features and state-of-the-art ML methodologies in the prediction of the mechanical properties of polymer composites, such as tensile strength. This includes embedding several ensemble models, including Random Forest and Gradient Boosting, kernel methods such as SVR, neural networks, graph-based approaches, while it applies principled hyperparameter tuning and uncertainty quantification, along with model-interpretability tools such as SHAP and systematic ablation to identify the most important material and processing factors. The proposed methodology is demonstrated with curated, publicly available datasets, and we discuss means for synthetic data augmentation, cross-validation protocols, assessment of model robustness, and best practices in reporting results in a reproducible way. The results show that data-driven models reduce prediction error and speed up the processes of materials selection in view of Net Zero targets, given limited available experimental iterations; this informs intelligent lightweighting of renewable energy components. Furthermore, the study highlights the capability of machine learning models to capture complex nonlinear relationships between composition, processing parameters, and mechanical response that are difficult to address using conventional trial-and-error approaches. By reducing reliance on extensive experimental campaigns, the proposed framework supports faster design cycles and more efficient utilization of material and energy resources in renewable energy applications.
The intersection of machine learning (ML) and electrochemical research is catalyzing transformative advancements in energy storage and conversion technologies. This review critically examines the role of ML and deep learning (DL) in optimizing electrochemical systems, focusing on batteries, supercapacitors, fuel cells, and sensors. ML-driven approaches facilitate accelerated material discovery, precise property predictions, and enhanced device performance monitoring, surpassing conventional trial-and-error methodologies. Integrating computational materials science, including density functional theory (DFT) and molecular dynamics (MD), with ML enables predictive modelling of electrochemical processes at an unprecedented scale. However, challenges such as data heterogeneity, model interpretability, and computational cost continue to limit widespread adoption. This review identifies key strategies to overcome these barriers, including establishing standardized data repositories, developing hybrid physics-informed ML models, and implementing explainable AI (XAI) for enhanced model transparency. By addressing these challenges, ML has the potential to drive the next wave of breakthroughs in electrochemical energy storage and conversion, accelerating the transition toward a sustainable and energy-secure future.
Unknown authors· Journal of the Electrochemic...· 0 citations
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