Jun 2026· Ceramist· Vol 29, pp. 211-228· 0 citations· 3 references
TL;DR
This review examines the latest progress through three synergistic axes: acceleration of quantum mechanical simulations via universal machine learning interatomic potentials trained on large-scale density functional theory datasets, materials-aware artificial intelligence leveraging large language models, synthetic narrative datasets, and contrastive multimodal learning for zero-shot materials retrieval.
Abstract
The rapid advancement of Artificial intelligence (AI) and machine learning is transforming materials discovery, offering the potential to accelerate the traditionally slow cycle of materials development. This review examines the latest progress through three synergistic axes: (1) acceleration of quantum mechanical simulations via universal machine learning interatomic potentials trained on large-scale density functional theory datasets, (2) materials-aware artificial intelligence leveraging large language models, synthetic narrative datasets, and contrastive multimodal learning for zero-shot materials retrieval, and (3) autonomous laboratories integrating Bayesian optimization and reinforcement learning for closed-loop experimental optimization. We discuss the specific opportunities and remaining challenges for applying these methodologies to ceramic materials and present a vision toward fully autonomous AI-driven materials discovery.
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
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 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 emerging AI methodologies for accelerated materials discovery, with particular emphasis on how computational design, data infrastructure, synthesis planning, and autonomous experimentation can be connected into experimentally grounded workflows.
Jaehwan Choi, Seongmin Kim, Junkil Park et al.· Chemical Reviews· 1 citation
The rapid advancement of artificial intelligence (AI) and machine learning (ML) is reshaping scientific discovery through an information-theoretic par-adigm. Modern large-scale models function as entropy-compression engines: they extract low-entropy, transferable representations from high-dimensional and noisy data, reducing epistemic uncertainty in complex physical systems. This perspective motivates growing interest in entropy- oriented and infor-mation-theoretic approaches to model compression, generalization, and ro-bustness. Soft matter materials—particularly multiscale and compositionally diverse hydrogels—naturally constitute a high-entropy design landscape. Traditional intuition-driven exploration struggles to navigate such spaces or capture nonlinear, cross-scale dependencies reminiscent of complex-system behaviors. To address this challenge, we develop an intelligent hydrogel de-sign framework grounded in information theory and multiscale representa-tion learning. Feature engineering acts as an entropy-reduction step, com-pressing raw variables into structured representations that support stable multi-task learning. A multi-objective Bayesian optimization scheme with Gaussian-process surrogates and a q-Expected Hypervolume Improvement (q-EHVI) acquisition function further performs targeted information acquisi-tion, maximizing posterior entropy reduction while efficiently expanding the Pareto front. A self-evolving feedback loop integrates AI prediction with ex-perimental validation, allowing each iteration to reduce system-level uncer-tainty and refine representations across scales. Incorporating entropy-based and chaos-inspired metrics provides additional diagnostics for robustness and sensitivity, aligning the workflow with emerging information-theoretic prin-ciples used in reliable large-model development. Overall, this study estab-lishes an entropy-aware and representation-driven paradigm for the intelli-gent design of soft functional materials, offering a generalizable pathway for accelerating discovery and enhancing extrapolation capability.
Yisheng Lin· Poster Volume 0008 The 2026...· 0 citations
This work pursues a multi-tier development strategy in which machine learning (ML) algorithms are combined with exact physical symmetries and constraints to significantly accelerate computations of electronic structure and atomistic dynamics.