Abstract
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 $\delta$-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.
Abstract (translated)
自主实验能够通过结合人工智能（AI）与模块化机器人平台来探索广泛组合的化学和工艺空间，从而加速材料开发。这样的自驱动实验室不仅可以提高重复性实验的吞吐量，还可以整合人类的专业知识以推动研究朝向用户定义的目标发展，包括改善材料性能指标等。我们为科学自主推理代理（SARA）提供了一个自主材料合成扩展，利用自动化概率相位标签算法提供的相位信息来加快目标相位区域的搜索速度。通过将人类输入纳入扩大的SARA-H（具有人在回路中的SARA）框架中，我们可以增强底层推理过程的有效性。使用合成基准测试，我们展示了我们的AI实现效率，并证明了人类输入可以显著提高采样效率。 我们在几种氧化物材料系统（包括Bi₂O₃、SnOₓ和Bi-Ti-O）的薄膜样品上进行实验主动学习活动，采用横向梯度激光脉冲退火来合成并动力学捕获亚稳态相。我们展示了人在回路中的自主实验在Bi-Ti-O系统的实用性，在该系统中，我们识别了大量的加工领域以稳定δ-Bi₂O₃和Bi₂Ti₂O₇，并探索了滞留依赖的三元氧化物相位行为，并提供了证据支持预测：即对TiO₂进行Bi阳离子置换掺杂可抑制亚稳态锐钛矿向体心结构金红石相变。我们开发的自主方法能够发现新材料并深入理解材料合成和性质的新知识。
URL
https://arxiv.org/abs/2601.08185
