Autonomous experimentation enabled by artificial intelligence (AI) offers a new paradigm for accelerating scientific discovery. Non-equilibrium materials synthesis is emblematic of complex, resource-intensive experimentation whose acceleration would be a watershed for materials discovery and development. The mapping of non-equilibrium synthesis phase diagrams has recently been accelerated via high throughput experimentation but still limits materials research because the parameter space is too vast to be exhaustively explored. We demonstrate accelerated synthesis and exploration of metastable materials through hierarchical autonomous experimentation governed by the Scientific Autonomous Reasoning Agent (SARA). SARA integrates robotic materials synthesis and characterization along with a hierarchy of AI methods that efficiently reveal the structure of processing phase diagrams. SARA designs lateral gradient laser spike annealing (lg-LSA) experiments for parallel materials synthesis and employs optical spectroscopy to rapidly identify phase transitions. Efficient exploration of the multi-dimensional parameter space is achieved with nested active learning (AL) cycles built upon advanced machine learning models that incorporate the underlying physics of the experiments as well as end-to-end uncertainty quantification. With this, and the coordination of AL at multiple scales, SARA embodies AI harnessing of complex scientific tasks. We demonstrate its performance by autonomously mapping synthesis phase boundaries for the Bi$_2$O$_3$ system, leading to orders-of-magnitude acceleration in establishment of a synthesis phase diagram that includes conditions for kinetically stabilizing $\delta$-Bi$_2$O$_3$ at room temperature, a critical development for electrochemical technologies such as solid oxide fuel cells.
翻译:由人工智能(AI)促成的自主实验为加速科学发现提供了一个新的范式。非平衡材料合成是复杂、资源密集型实验的象征,加速将是材料发现和开发的分水岭。最近,通过高吞量实验加快了非平衡合成阶段图的绘制工作,但由于参数空间太广,无法进行详尽的探索,仍然限制了材料研究。我们展示了通过科学自主解释代理(SARA)管理的等级自主实验加速合成和探索元材料的情况。SARA结合了机器人材料合成和定性以及高效显示处理阶段图结构的人工智能方法的等级。SAA设计横向梯度激光加速了平行材料合成(lg-LSA)试验,并运用光谱光谱分析仪来迅速确定阶段的转变。对多维参数空间进行了高效的探索,在先进机器学习模型的基础上建立了嵌套式(AL)循环,其中包括固体物理物理物理学以及最终的不确定性量化。在多个比例范围内对AL值的磁极激光激光激光测算系统进行了稳定度分析,从而将精度的精度分析系统用于复杂的合成阶段。