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.
Abstract
Artificial intelligence (AI) has emerged as a fruitful tool in materials science, enabling accelerated discovery, characterization, and optimization of functional materials. Among ferroelectrics, fluorite‐doped hafnium oxide attracts exceptional attention due to its CMOS compatibility, scalability, and robust ferroelectricity at a few nanometers in thickness. However, understanding and optimizing the relationships between metastable ferroelectric phase formation and property‐processing remain challenging due to the multidimensional parameter space governing its synthesis and performance. 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
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‐based ferroelectrics. While such approaches have advanced understanding of structure‐property relationships, AI‐driven synthesis process optimization, and closed‐loop synthesis remain underexplored. We outline current achievements, identify critical gaps, and propose next steps that integrate multimodal data fusion, active learning, and combinatorial synthesis to enable predictive design and autonomous optimization of next‐generation hafnia‐based ferroelectrics.
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
Machine learning (ML) is transforming the development of luminescent materials, including quantum dots, rare‐earth‐doped phosphors, and transition‐metal‐doped phosphors, by addressing the limitations inherent in traditional trial‐and‐error methodologies. First‐generation ML models accurately predict single properties such as bandgaps and Debye temperature, enabling virtual material screening and revealing underlying physical mechanisms. Second‐generation approaches use ensemble learning and Bayesian optimization to optimize multiple objectives simultaneously, such as quantum efficiency, thermal stability, chromaticity, and environmental compliance, leading to synergistic performance enhancements. Next‐generation autonomous laboratories, which integrate ML‐driven predictions with robotic synthesis and real‐time characterization, create closed‐loop systems that significantly accelerate the materials discovery process and compress traditional development cycles. Despite these advancements, significant challenges persist, including poor data quality and standardization, limited model generalizability across different material systems, and the low interpretability of complex “black‐box” models. Overcoming these limitations will fully harness the potential of this ML‐driven ecosystem, facilitating the intelligent design of next‐generation luminescent materials for advanced displays, energy‐efficient lighting, and biomedical applications.
The integration of artificial intelligence and machine learning into chemical engineering represents a paradigm transformation that fundamentally reconceptualizes practice across molecular, process, and industrial scales. This comprehensive narrative review synthesizes recent advances in AI/ML methodologies, including graph neural networks, physics-informed neural networks, deep reinforcement learning, and generative artificial intelligence – and critically evaluates their applications spanning molecular property prediction, catalyst design, pharmaceutical development, reactor optimization, process control, and sustainability initiatives. Contemporary AI/ML approaches demonstrate unprecedented capabilities in navigating complex multiscale phenomena while maintaining computational tractability and physical interpretability. Landmark achievements include 71% reduction in experimental iterations for reaction optimization through deep reinforcement learning, 98% accuracy in predictive maintenance using LSTM-based fault detection, sub-1% prediction errors in virtual metrology for semiconductor manufacturing, and substantial improvements incarbon capture efficiency through machine learning-guided materials discovery. Physics-informed neural networks address the critical challenge of plant-model mismatch by synergistically integrating mechanistic knowledge with data-driven learning, enabling extrapolation beyond training domains while respecting conservation laws. Explainable AI techniques, particularly SHAP analysis, enhance operational acceptance by 52% in safety-critical applications through transparent decision-making pathways. Despite remarkable progress, persistent challenges remain in data quality and standardization, model interpretability for regulatory compliance, computational scalability for real-time control, and integration with legacy industrial infrastructure. The review identifies transformative future directions including multi-modal learning frameworks, transfer learning for data-scarce applications, quantum machine learning for molecular design, and human-AI collaborative systems. Successful deployment demands interdisciplinary collaboration uniting chemical engineering domain expertise with computational intelligence, guided by principles of transparency, reproducibility, and responsible innovation to address sustainability imperatives while maintaining operational excellence and safety in chemical manufacturing.
Fatemeh Valizadeh Hajidehi, Azam Mina· Chemical and Process Enginee...· 0 citations
Nanozymes, a class of nanomaterials with intrinsic enzyme‐like catalytic activities, have emerged as promising platforms for catalysis‐driven diagnostics, therapeutics, and environmental sensing. Yet the multiscale structural heterogeneity of nanozymes and the nonlinear coupling between physicochemical descriptors and catalytic performance have hindered the establishment of predictive structure‐activity relationships. The rapid integration of artificial intelligence, particularly machine learning and deep learning, is reshaping this landscape by enabling systematic data integration, descriptor engineering, quantitative performance prediction, and mechanism‐informed optimization. This Review summarizes computational and data‐driven paradigms for rational nanozyme engineering, encompassing cross‐source data infrastructures, physicochemical feature representation, model architectures, and closed‐loop experimental validation. Representative advances in AI‐guided nanozyme design and high‐throughput computational screening are highlighted, alongside emerging applications in intelligent diagnostics, precision therapeutics, and environmental surveillance. By bridging materials science, catalysis theory, and data intelligence, AI‐enabled strategies provide a coherent framework for accelerating nanozyme discovery and functional optimization, paving the way toward more precise and scalable catalytic nanomaterials.
Xiaolin Guo, Hao Zhang, Zihan Zhao et al.· Journal of Intelligent Medic...· 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
Two‐Stage Material Screening (TSMS) is developed, an AI‐driven framework that integrates density functional theory (DFT) computations, an active‐learning‐guided experimental feedback loop, and mechanistic interpretation to enable rapid discovery and systematic evaluation of promising electrocatalysts.
Xueyu Hu, Yucun Zhou, Haoyu Li et al.· Advances in Materials· 0 citations
