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.
Abstract
From first-principles calculations to machine learning-driven materials discovery, computational methods enhance our understanding of material behavior under different conditions. Furthermore, these modern computational tools allow scientists to explore vast design spaces more efficiently. Namely, by simulating material properties before synthesis, researchers can rapidly screen potential candidates, optimize structures, and uncover novel materials that might not have been feasible through traditional experimentation alone. As the power of computation continues to grow, the role of computational tools in solving complex materials science challenges will only expand, accelerating innovation and transforming the way materials are understood, discovered, and developed.
I will present examples from our research that illustrate how we integrate high-throughput computing with machine learning (ML) and artificial intelligence (AI) to tackle complex challenges in materials science [1, 2]. I will first discuss recent progress in the development of automated computational workflows that support large-scale screening of materials for targeted properties, such as high electro-conversion or stability against electrochemical dissolution. These frameworks also allow us to develop large databases of relevant materials properties. When combined with modern ML tools, these databases can be used to train surrogate ML-models capable of screening millions of candidate chemistries to identify the ones with optimal reactivity or stability. Furthermore, I will illustrate the use of interpretable ML in materials research that aims to enhance explainability of predictive ML models, enabling understanding of the underlying factors influencing design decisions. Lastly, I will present our 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.
[1] M. Davis, W. Kort-Kamp, E. F. Holby, P. Zelenay, and I. Matanovic, Computational Screening of Transition Metal-Nitrogen-Carbon Materials as Electrocatalysts for CO
2
Reduction.
Electrochimica Acta 510,
145357(2025).
[2] M. Davis, W. Kort-Kamp, I. Matanovic, P. Zelenay and E. F. Holby, Design of Amine-Functionalized Materials for Direct Air Capture Using Integrated High-Throughput Calculations and Machine Learning, accepted in
Communications Chemistry
(2025).
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 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.
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
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.
Jacqueline M. Cole· Advances in Materials· 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
Artificial intelligence (AI) is transforming the way materials are designed, understood, and manufactured. This Perspective examines how recent advances in data‐driven modeling, high‐performance simulation, and autonomous experimentation are converging to accelerate the discovery of functional materials for next‐generation technologies—from energy storage and biomedicine to nanoelectronics and quantum devices. We outline ongoing strategies to embed AI across the materials design workflow—from synthesis and characterization to large‐scale simulations enabled by machine learning techniques and approaching ab initio accuracy—and discuss key challenges that remain on the path toward intelligent (bio)materials discovery.
Cristiano Malica, Kostya S. Novoselov, Seongmin Kim et al.· Advanced Intelligent Systems· 0 citations