A seven-tier physics-informed inverse-design framework integrating chemistry, thermodynamics, transport, electrochemistry, durability, cell compatibility, and manufacturability is proposed, providing a general framework for AI-enabled autonomous materials discovery across energy-storage materials and other functional materials.
Abstract
The discovery of next-generation energy-storage materials is increasingly limited by the complexity of the underlying design problem rather than by computational capability alone. Porous transition-metal oxides represent a particularly challenging class of battery materials because their performance emerges from coupled interactions among crystal chemistry, pore architecture, ion transport, electrochemistry, electro-chemo-mechanics, synthesis, manufacturing, and battery-system operation. Recent advances in generative artificial intelligence (AI) have demonstrated remarkable capabilities for generating chemically plausible crystal structures. However, current approaches remain largely focused on crystallographic validity and thermodynamic stability. This perspective presents a roadmap for advancing generative AI beyond crystal generation toward physics-informed, application-aware, and synthesis-aware inverse design. Using porous oxide electrodes as a representative materials platform, we propose a seven-tier physics-informed inverse-design framework integrating chemistry, thermodynamics, transport, electrochemistry, durability, cell compatibility, and manufacturability. We further identify the"Missing Data Problem"as a fundamental bottleneck limiting application-aware AI and introduce an autonomous knowledge-generation framework supported by a Porous Oxide Energy Materials Ontology and a continuously evolving"Knowledge Base". Together, these concepts establish the foundation for Synthesis-Aware, Closed-Loop Autonomous Discovery, providing a general framework for AI-enabled autonomous materials discovery across energy-storage materials and other functional materials.
Organic electrochemical energy materials (OEEMs) offer a vast design space for rechargeable batteries, redox‐flow batteries, supercapacitors, and mixed ionic–electronic devices, yet their performance depends on tightly coupled thermal, redox, transport, mechanical, morphological, and interfacial properties that can rarely be optimized independently. This review examines how artificial intelligence (AI) is reshaping the computational design and modeling of these materials, tracing the field's shift from small redox‐active molecules toward polymeric electrolytes and electrodes. We cover digital representations and data sources, data‐driven property prediction, machine‐learning interatomic potentials, generative molecular and polymer design, and large‐language model‐assisted (agentic) workflows. It is written for researchers entering the area from either direction, whether computational chemists curious about AI, or machine learning (ML) researchers new to electrochemical materials. Rather than an exhaustive survey, we give a selective snapshot of fast‐moving literature, using representative examples to draw out practical, transferable lessons, and we provide a concise checklist of best practices to help newcomers evaluate and report their own work before publication. We close with our perspective on where the field is heading, together with the persistent challenges of experimental data quality, validation, polymer representation, reproducibility, and modeling integration. The accompany online dataset catalog is maintained as a living community resource that evovles with the rapidly expanding OEEM data landscape.
Zhan-Yun Zhang, Giannis Savvas, Ji-Chen Li et al.· Macromolecular materials and...· 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
This Perspective traces the evolution of thermal transport science from its empirical origins through the current AI-driven renaissance, and examines the limitations of conventional density-functional-theory-based phonon workflows, the emergence of universal machine learning interatomic potentials (uMLPs), graph-neural-network screening architectures such as the Crystal Attention Graph Neural Network (CATGNN), and generative inverse-design frameworks.
Ming Hu· Journal of Materials Science...· 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
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
The solid-state electrolytes (SSE) are gaining tremendous attention in designing rechargeable batteries with remarkable energy density and safety features for next-generation energy storage device applications. The rational design of SSE with promising ionic conductivity, higher electrochemical stability windows, and stable electrode-electrolyte interfaces remain a formidable challenge, traditionally hindered by trial-and-error experimentation and computationally expensive theoretical simulations. Here, we systematically review the recent breakthroughs in the artificial intelligence (AI)-driven design of SSE, spanning electrochemical stability and ionic conductivity domains, with a particular focus on how machine learning (ML) and deep learning (DL) are fundamentally transforming the discovery and optimization landscape. We critically discuss the synergy between first-principles density functional theory (DFT), molecular dynamics (MD) simulations, and advanced AI algorithms including supervised and unsupervised learning (SL, UL), graph neural networks (GNNs), and Machine Learning Interatomic Potentials (MLIP) that collectively enable accurate prediction of ionic conductivity, elucidation of ion transport mechanisms, and high-throughput screening (HTS) of vast chemical spaces. Emphasis is placed on descriptor engineering that bridges atomic-level structural features (e.g., lattice parameters, activation energies, defect chemistry) with macroscopic electrochemical performance, as well as the emerging paradigm of closed-loop, self-driving laboratories for autonomous materials discovery. Furthermore, AI-guided strategies have demonstrated remarkable interfacial ionic transport mechanism. Despite these transformative advances, persistent challenges including data scarcity, limited descriptor transferability, discrepancies between theoretical predictions and experimental realization, remain significant challenges. Looking forward, the convergence of AI with high-throughput experimentation and multiscale modeling promises to redefine SSE discovery, accelerating the deployment of high-performance all solid-state batteries (ASSBs) for sustainable energy storage.
Jiaying He, Zama Jan, Heqin Guo et al.· AI for Materials· 0 citations
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