AI 中文总结
本综述针对热化学燃料生产中复杂氧化物优化的难题，提出整合高通量计算、自动化技术与机器学习的闭环自主设计方案，梳理相关进展并指出关键挑战。
AI 中文摘要
两步法热化学燃料生产（包括H₂O和CO₂分解）为可持续燃料制造提供了一条有前景的途径，其性能由能实现循环氧化还原反应的氧化还原活性氧化物决定。最大化热-燃料转换效率需要材料同时满足多个严格的热力学和动力学目标，这些要求正推动材料设计向复杂的多阳离子氧化物发展，如混合阳离子萤石、钙钛矿和高熵氧化物，其组成、缺陷化学、相稳定性和形貌需协同优化，这形成了传统试错法难以应对的材料优化难题。本综述认为，热化学燃料生产为自主材料设计和优化提供了极具吸引力的前沿领域。我们首先分析了氧化还原活性复杂氧化物难以开发的原因，包括多维相空间、苛刻的操作条件以及相互竞争的功能目标；随后探讨了如何将高通量计算、自动化合成、表征与测试以及机器学习整合到闭环工作流程中以应对这些挑战；基于更广泛的氧化物材料研究，我们将近期进展整理成复杂氧化物优化的能力路线图，涵盖组成多样的合成、 operando表征、机器人测试、操作条件计算以及多目标优化；最后，我们概述了构建用于热化学燃料生产材料开发的自改进材料开发平台面临的关键实验、计算和数据挑战。
英文摘要
Two-step thermochemical fuel production, including H2O and CO2 splitting, offers a promising route to sustainable fuel manufacturing, with performance governed by redox-active oxides that enable cyclic reduction-oxidation reactions. Maximizing thermal-to-fuel conversion efficiency demands materials that simultaneously satisfy multiple stringent thermodynamic and kinetic targets. Addressing these requirements has increasingly driven materials design toward complex, multi-cation oxides, such as mixed-cation fluorites, perovskites, and high-entropy oxides, wherein composition, defect chemistry, phase stability, and morphology should be co-optimized. This creates a challenging materials optimization problem that is poorly suited to traditional trial-and-error approaches. In this review, we argue that thermochemical fuel production provides a compelling frontier for autonomous materials design and optimization. We first examine why redox-active complex oxides are difficult to develop, owing to multidimensional phase spaces, harsh operating conditions, and competing functional targets. We then discuss how high-throughput computation, automated synthesis, characterization and testing, and machine learning can be integrated into closed-loop workflows to address these challenges. Building on broader oxide materials research, we organize recent progress into a capability roadmap for complex-oxide optimization, spanning compositionally diverse synthesis, operando characterization, robotic testing, operation-condition computation, and multi-objective optimization. Finally, we outline key experimental, computational, and data challenges for building self-improving materials development platforms for materials development in thermochemical fuel production.
Comments31 pages, 3 figures, 1 table
