This presentation will explore the integration of AI-driven methods and advanced simulation techniques to accelerate the development of high-performance heterogeneous functional materials to enable the development of next-generation devices with high efficiency, scalability, and durability.
Abstract
The design and optimization of heterogeneous functional materials for energy conversion and storage devices is a complex challenge that requires a deep understanding of material properties across multiple scales. Recent advancements in artificial intelligence (AI) and computational simulations are providing new avenues for designing these materials with unprecedented precision and performance. This presentation will explore the integration of AI-driven methods and advanced simulation techniques to accelerate the development of high-performance heterogeneous functional materials. AI-based approaches, including machine learning algorithms and optimization techniques, are increasingly used to analyze vast datasets from material properties, design strategies, and experimental results to identify new material combinations and predict their behavior in electrochemical applications.
I will highlight novel AI-driven models that can predict the emergent properties of materials. Additionally, I will discuss the integration of simulation techniques like molecular dynamics, density functional theory (DFT), and continuum modeling to simulate the behavior of materials at different length scales, from atomic to macro. These simulations, when coupled with AI-driven optimization algorithms, enable the design of materials that enhance performance and efficiency in energy conversion and storage applications.
I will conclude by discussing the future directions of AI and simulation integration in the design of heterogeneous functional materials, focusing on their potential to enable the development of next-generation devices with high efficiency, scalability, and durability.
A platform that integrates artificial intelligence (AI) with advanced computational simulations beyond DFT, designed to accelerate the design and optimization of materials for high-performance applications in electronics, photonics, and energy technologies is presented.
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
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
The accelerating global demand for high-performance energy storage systems has stimulated significant research into advanced polymer composites as next-generation electrolytes, electrode binders, and functional membranes for batteries, supercapacitors, and photovoltaic devices. However, the vast compositional and structural design space of polymer materials presents formidable challenges for conventional trial-and-error discovery strategies, which remain slow, costly, and biased by prior expert knowledge. Machine learning (ML) and artificial intelligence (AI) have emerged as transformative tools for navigating this complexity, enabling rapid prediction of electrochemical properties, de novo design of polymer electrolytes, and precise optimization of nanostructures for supercapacitors and batteries. This review systematically examines the application of ML techniques, including graph neural networks, Bayesian optimization, variational autoencoders, and transformer-based language models, for the discovery of energy storage polymer composites. The discussion critically evaluates ML-driven advancements across lithium-ion batteries, flexible energy storage devices, and solar energy materials, drawing on quantitative performance benchmarks reported in the primary literature. Emerging strategies such as active learning, multi-fidelity data fusion, physics-informed neural networks, and polymer-specific foundation models are discussed alongside persistent challenges related to data scarcity, model interpretability, and the translation gap between computational prediction and experimental synthesis. The review further addresses the landscape of open polymer property databases, the role of autonomous closed-loop experimentation in accelerating materials discovery, and the importance of reproducible, well-documented machine learning pipelines for the field to mature beyond proof-of-concept demonstrations. By consolidating evidence from verified primary sources and presenting original comparative analyses across methods and application domains, this review provides researchers, materials scientists, and computational chemists with an actionable, evidence-based perspective on the current state and future trajectory of AI-accelerated, sustainable energy storage polymer composite discovery.
Manas Kumar Yogi, D. Uma, Yamuna Mundru et al.· Journal of Polymer & Composi...· 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
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
