在自主扫描探头显微镜中的解释性和人类干预
Yongtao Liu1, Maxim A Ziatdinov1,2, Rama K Vasudevan1
1Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.
Patterns (New York, N.Y.)
|November 30, 2023
概括
我们在材料科学中开发了一种基于机器学习 (ML) 的自主实验 (AEs) 的实验后分析策略. 这种方法提供实时指标,以理解和指导机器学习驱动的实验工作流程.
科学领域:
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
- 实验设计 实验设计
背景情况:
- 基于机器学习 (ML) 的自主实验 (AE) 的广泛采用需要强大的工作流分析和干预策略.
- 目前的方法缺乏实时指标,以了解ML驱动的实验过程的进展.
研究的目的:
- 介绍和演示基于深度内核学习的自主扫描探针显微镜的实验后分析策略.
- 为AEs的积极学习过程提供实时和实验后指标.
- 为了说明这项策略对循环中的人类AE的适用性.
主要方法:
- 开发了一种针对自主扫描探针显微镜深度内核学习的实验后分析策略.
- 实行实时和实验后指标,以监测积极学习的进展.
- 证明了该战略与循环中的人类AE集成,用于高级别的政策制定和低级别的决策.
主要成果:
- 拟议的战略产生了有效的实时和实验后指标,用于AE的积极学习.
- 成功地应用了人类在循环中的自主实验方法,实现了高效的人类-人工智能合作.
- 验证了该方法对各种材料表征和合成应用的普遍性.
结论:
- 开发的实验后分析策略增强了基于ML的AE的理解和控制.
- 这种方法促进了人类专业知识的无整合到自主实验工作流中.
- 该策略可适应各种实验技术,包括组合图书馆分析.
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