AI 中文总结
本文针对多孔氧化物能源材料的自主发现问题，提出整合多维度物理信息的七层逆向设计框架与知识生成体系，为AI驱动的储能材料自主发现提供通用方案。
AI 中文摘要
下一代储能材料的发现日益受到底层设计问题的复杂性限制，而非仅受计算能力的制约。多孔过渡金属氧化物是一类极具挑战性的电池材料，其性能源于晶体化学、孔结构、离子传输、电化学、电化学力学、合成、制造及电池系统运行之间的耦合相互作用。生成式人工智能（AI）的最新进展已展现出生成化学上合理晶体结构的卓越能力，但当前方法仍主要聚焦于晶体学有效性和热力学稳定性。本文提出了将生成式AI从晶体生成推进至物理信息、应用感知及合成感知逆向设计的路线图。以多孔氧化物电极作为代表性材料平台，我们提出了一个七层物理信息逆向设计框架，整合了化学、热力学、传输、电化学、耐久性、电池兼容性及可制造性。我们进一步指出“缺失数据问题”是限制应用感知AI的根本瓶颈，并引入了由多孔氧化物能源材料本体及持续演进的“知识库”支持的自主知识生成框架。这些概念共同为合成感知、闭环自主发现奠定了基础，为AI赋能的储能材料及其他功能材料领域的自主材料发现提供了通用框架。
英文摘要
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.
Comments36 pages, 11 figures
