Autonomous experimentation holds the potential to accelerate materials development by combining artificial intelligence (AI) with modular robotic platforms to explore extensive combinatorial chemical and processing spaces. Such self-driving laboratories can not only increase the throughput of repetitive experiments, but also incorporate human domain expertise to drive the search towards user-defined objectives, including improved materials performance metrics. We present an autonomous materials synthesis extension to SARA, the Scientific Autonomous Reasoning Agent, utilizing phase information provided by an automated probabilistic phase labeling algorithm to expedite the search for targeted phase regions. By incorporating human input into an expanded SARA-H (SARA with human-in-the-loop) framework, we enhance the efficiency of the underlying reasoning process. Using synthetic benchmarks, we demonstrate the efficiency of our AI implementation and show that the human input can contribute to significant improvement in sampling efficiency. We conduct experimental active learning campaigns using robotic processing of thin-film samples of several oxide material systems, including Bi$_2$O$_3$, SnO$_x$, and Bi-Ti-O, using lateral-gradient laser spike annealing to synthesize and kinetically trap metastable phases. We showcase the utility of human-in-the-loop autonomous experimentation for the Bi-Ti-O system, where we identify extensive processing domains that stabilize $δ$-Bi$_2$O$_3$ and Bi$_2$Ti$_2$O$_7$, explore dwell-dependent ternary oxide phase behavior, and provide evidence confirming predictions that cationic substitutional doping of TiO$_2$ with Bi inhibits the unfavorable transformation of the metastable anatase to the ground-state rutile phase. The autonomous methods we have developed enable the discovery of new materials and new understanding of materials synthesis and properties.

翻译：自主实验通过将人工智能（AI）与模块化机器人平台相结合，探索广阔的组合化学与工艺空间，有望加速材料研发。此类自驱动实验室不仅能提升重复性实验的通量，还能融入人类领域专业知识，以驱动搜索朝向用户定义的目标（包括改进的材料性能指标）发展。我们为科学自主推理智能体（SARA）提出了一种自主材料合成扩展方案，利用自动化概率性物相标记算法提供的物相信息，加速针对目标物相区域的搜索。通过将人类输入纳入扩展的SARA-H（带人机回路的SARA）框架，我们提升了底层推理过程的效率。使用合成基准测试，我们证明了AI实现的高效性，并表明人类输入能显著提升采样效率。我们利用机器人处理多种氧化物材料体系（包括Bi$_2$O$_3$、SnO$_x$和Bi-Ti-O）的薄膜样品，通过横向梯度激光尖峰退火合成并动力学捕获亚稳相，开展了实验性主动学习研究。我们展示了人机回路自主实验在Bi-Ti-O体系中的应用价值：识别出可稳定$δ$-Bi$_2$O$_3$和Bi$_2$Ti$_2$O$_7$的广阔工艺区间，探索了保温时间依赖的三元氧化物相行为，并提供了证据证实了先前预测——即用Bi对TiO$_2$进行阳离子置换掺杂会抑制亚稳态锐钛矿向基态金红石相的不利转变。我们所开发的自主方法能够实现新材料的发现以及对材料合成与性能的新理解。