Active learning - the field of machine learning (ML) dedicated to optimal experiment design, has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics [1]. In this work we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate autonomous research methodology (i.e. autonomous hypothesis definition and evaluation) that can place complex, advanced materials in reach, allowing scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. Additionally, this robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. We used the real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) at the synchrotron beamline to accelerate the fundamentally interconnected tasks of rapid phase mapping and property optimization, with each cycle taking seconds to minutes, resulting in the discovery of a novel epitaxial nanocomposite phase-change memory material.
翻译:积极学习 -- -- 机器学习领域(ML)专门致力于最佳实验设计,早在18世纪拉普特(Laplace)利用它来指导天体力学[1]的发现,在科学领域发挥了一定的作用。在这项工作中,我们把一个闭路、积极学习驱动的自主系统集中到另一个重大挑战上,即发现先进材料来对付极其复杂的合成-工艺-结构-财产景观。我们展示了自主研究方法(即自主假设定义和评价),可以将复杂、先进的材料送到手边,让科学家在他们的研究中更加聪明、学习更快和花费较少的资源,同时增进对科学成果和机器学习工具的信任。此外,这种机器人科学使科学能够超越网络,减少科学家与实验室实际分离的经济影响。我们在同步线上使用了实时闭路、自主材料探索和优化系统(CAMEO)来加速快速阶段绘图和财产优化这一基本相互关联的任务,每个周期要花几秒到几分钟,从而发现新型的神经合成阶段变化记忆材料。