Aug 2026· Advances in Materials· pp.
e74491
· 0 citations· 96 references
Medicine
TL;DR
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.
Abstract
Materials science is underpinned by structure-property relationships that govern the function of a material. These relationships can be encoded into algorithms and integrated into machine-learning models that enable the prediction of materials and their cognate properties. However, machine-learning models are largely being trained on computed data owing to a worldwide shortage of real-world (experimental) datasets. This review describes how to capture and collate experimental data from scientific literature using artificial-intelligence (AI) methods to produce materials-domain-specific datasets or language models. Their application in AI-driven enquiries that facilitate progress in energy-sustainable materials science is then illustrated via six case studies that cover: training machine-learning models, data-driven materials discovery, optimizing manufacturing processes, mapping phases of materials, forecasting materials-centric research trends, and classifying types of materials using automated prompt engineering. The future of materials-domain-specific datasets, language models, and decision-making workflows using AI agents is then envisioned for the energy sector. The intrinsic challenges of accessing historical dark data in materials science are then 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. These opportunities are illustrated for energy-sustainable materials science, especially the photovoltaic and battery industries.
Machine learning (ML) in materials science refers to computational methods that learn statistical, structural, or physics-informed relationships from experimental, computational, and literature-derived materials data. These methods are used to predict materials properties, identify structure–property and process–structure–property–performance relationships, discover candidate materials, optimize synthesis and processing routes, and guide functional applications. ML is narrower than artificial intelligence (AI), which also includes broader reasoning, planning, search, and automation capabilities. It is also distinct from materials informatics, which is the wider data-centered framework that includes databases, descriptors, metadata, workflows, visualization, and knowledge management. ML can complement high-throughput computation by building surrogate models from density functional theory, finite-element simulation, molecular dynamics, or experimental data, but it is not identical to high-throughput screening itself. Unlike conventional physics-based modeling, which begins with explicit governing equations or mechanistic assumptions, ML usually infers predictive relationships from data; modern approaches increasingly combine both perspectives through physics-informed features, uncertainty quantification, and human expertise.
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.
Iyengar P.K· International Journal of Mod...· 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.
This work will present the current work on inverse material design, where AI methods—particularly generative pretrained transformers—are used to predict new material candidates based on desired properties, pushing the boundaries of materials innovation.
I. Gonzales, R. Ullberg, Andrew H Salij et al.· ECS Meeting Abstracts· 0 citations
This Perspective systematically discusses Physics-Grounded Materials AI (PhysMat AI) as a unifying perspective for integrating physical knowledge into materials intelligence through five complementary roles: physics as prior knowledge, descriptors, constraints, verifiers, and infrastructure.
Yuhang Wang, Qian Wang, Seong‐Hoon Jang et al.· Advanced Functional Material...· 0 citations
Machine learning models have become a powerful tool in materials science, accelerating the discovery process by accurately predicting molecular properties. However, the black‐box nature of many of these models makes it difficult to understand or validate their predictions. Consequently, explainable artificial intelligence (XAI) has emerged to make predictive models more transparent. This need for explainability is crucial in chemistry and materials science, as identified structure–property relationships can guide the design of novel molecules. However, there is still a lack of XAI benchmarks for materials science. To address this gap, this study presents a comparative quantitative and qualitative analysis of six selected XAI methods on molecular fingerprints, commonly used representations for material property prediction tasks. Moreover, this work explores the use of mean and robust rank aggregation (RRA) to combine multiple XAI approaches. The results reveal significant discrepancies in the feature importance rankings generated by different XAI methods, demonstrating that the choice of explainer can introduce bias and alter scientific interpretation in the material discovery process. Specifically, while general‐purpose tabular explainers like SHAP proved highly effective, chemistry‐specific counterfactuals often failed to correctly capture important features. Given the methods’ disagreements, aggregating the explanations, especially using RRA, can yield a well‐balanced compromise.
A. Przybyłowska, Joanna Neubauer, Monika Sztuder et al.· Advanced Intelligent Systems· 0 citations
