发表机构
University of California Berkeley; Lawrence Berkeley National Laboratory; Argonne National Laboratory; Bakar Institute of Digital Materials for the Planet, UC Berkeley; Vanderbilt University（加州大学伯克利分校; 劳伦斯伯克利国家实验室; 阿贡国家实验室; 加州大学伯克利分校巴克拉星球数字材料研究所; 范德堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出成本引导的自主固态合成（CASS）框架，结合物理模型与动态实验信息，在自驱动实验室中优化NASICON离子导体，78次试验发现18种有前景组成，其中两种电导率达0.7和0.3 mS/cm。
AI 中文摘要
在现有结构框架内进行元素替换是开发先进材料的一种广泛应用策略。然而，在保持相纯度的同时优化目标性能通常需要大量的试错，这在导航复杂设计空间时变得效率极低。在此，我们提出了一种策略，通过聚合成本函数同时且动态地评估成分依赖的合成可及性和目标性能，以指导真正自驱动和自学习模式下的自主实验。具体而言，我们开发了成本引导的自主固态合成（CASS）框架，并展示了其在发现Na超离子导体（NASICON）固态电解质中的应用。CASS成功优化了离子电导率和相纯度，在自主实验室A-Lab中进行的78次试验中识别出18种有前景的组成。其中，我们确定了两种快导NASICON，其总（体）离子电导率分别为0.7（3.96）和0.3（3.17）mS/cm。CASS的成功部署强化了将自驱动自主实验室与物理信息生成模型相结合以加速材料发现的潜力。此外，设计因素的可解释性，通过成功和失败合成的结果提供信息，使得模型改进和新化学见解的产生成为可能。
英文摘要
Elemental substitution within existing structural frameworks is a widely applied strategy for developing advanced materials. Yet, optimizing target properties while maintaining phase purity usually demands extensive trial-and-error, which becomes substantially inefficient when navigating a complex design space. Here, we introduce a strategy that simultaneously and dynamically assesses composition-dependent synthetic accessibility and target properties via aggregated cost functions that guide autonomous experimentation in a truly self-driving and self-learning mode. Specifically, we developed a cost-guided autonomous solid-state synthesis (CASS) framework and demonstrate its application in the discovery of Na superionic conductor (NASICON) solid electrolytes. CASS successfully optimizes ionic conductivity and phase purity, leading to the identification of 18 promising compositions in 78 trials conducted in an autonomous laboratory, the A-Lab. Among these, we identified two fast-conducting NASICONs yielding total (bulk) ionic conductivity of 0.7 (3.96) and 0.3 (3.17) mS/cm. The successful deployment of CASS reinforces the potential of coupling self-driving autonomous laboratories with physics-informed generative models to accelerate materials discovery. Moreover, the interpretability of the design factors, informed by outcomes from both successful and failed syntheses, enables model refinement and generation of new chemical insights.
Comments80 pages (45 pages main manuscript, 35 pages supplementary information), 7 main figures, and 19 supplementary figures
