Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI, also known as scientific AI or inductive bias AI, has grown from a focus on data analysis to now controlling experiment design, simulation, execution and analysis in closed-loop autonomous systems. The CAMEO (closed-loop autonomous materials exploration and optimization) algorithm employs scientific AI to address two tasks: learning a material system's composition-structure relationship and identifying materials compositions with optimal functional properties. By integrating these, accelerated materials screening across compositional phase diagrams was demonstrated, resulting in the discovery of a best-in-class phase change memory material. Key to this success is the ability to guide subsequent measurements to maximize knowledge of the composition-structure relationship, or phase map. In this work we investigate the benefits of incorporating varying levels of prior physical knowledge into CAMEO's autonomous phase-mapping. This includes the use of ab-initio phase boundary data from the AFLOW repositories, which has been shown to optimize CAMEO's search when used as a prior.
翻译:人造情报(AI)和更具体而言,机器学习的应用在过去几十年中大大扩展了对物理科学的应用,特别是,科学知情的AI,又称为科学AI或感应偏差AI,从数据分析的焦点发展到现在控制闭环自动系统实验设计、模拟、执行和分析,CAMEO(闭环自主材料探索和优化)算法利用科学AI处理两项任务:学习材料系统的组成结构关系和确定具有最佳功能特性的材料构成。通过整合这些材料,展示了跨组成阶段的加速材料筛选图,从而发现了一个在级最佳变化记忆材料。这一成功的关键是能够指导随后的测量,以最大限度地掌握关于组成结构关系或阶段地图的知识。在这项工作中,我们调查将不同程度的物理知识纳入CAMEO的自主阶段绘图的好处。这包括使用AFLOW储存库的ab-nitiotio阶段边界数据,这些数据已经显示在以前使用时优化CAMEO的搜索。