The modus operandi in materials research and development is combining existing data with an understanding of the underlying physics to create and test new hypotheses via experiments or simulations. This process is traditionally driven by subject expertise and the creativity of individual researchers, who “close the loop” by updating their hypotheses and models in light of new data or knowledge acquired from the community. Since the early 2000s, there has been notable progress in the automation of each step of the scientific process. With recent advances in using machine learning for hypothesis generation and artificial intelligence for decision-making, the opportunity to automate the entire closed-loop process has emerged as an exciting research frontier. The future of fully autonomous research systems for materials science no longer feels far-fetched. Autonomous systems are poised to make the search for new materials, properties, or parameters more efficient under budget and time constraints, and in effect accelerate materials innovation. This paper provides a brief overview of closed-loop research systems of today, and our related work at the Toyota Research Institute applied across different materials challenges and identifies both limitations and future opportunities.

中文翻译：

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走向自主材料研究：近期进展和未来挑战

作案手法在材料研究和开发中，将现有数据与对基础物理的理解相结合，通过实验或模拟来创建和测试新假设。这个过程传统上是由学科专业知识和个体研究人员的创造力驱动的，他们通过根据从社区获得的新数据或知识更新他们的假设和模型来“闭环”。自 2000 年代初以来，科学过程的每个步骤的自动化都取得了显着进展。随着使用机器学习进行假设生成和人工智能进行决策的最新进展，自动化整个闭环过程的机会已成为一个令人兴奋的研究前沿。完全自主的材料科学研究系统的未来不再遥不可及。自治系统准备好在预算和时间限制下更有效地搜索新材料、特性或参数，并实际上加速材料创新。本文简要概述了当今的闭环研究系统，以及我们在丰田研究所的相关工作应用于不同的材料挑战，并确定了局限性和未来机遇。