加速材料发现的自主实验室:社区调查与实践启示

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-05-31 DOI:10.1039/D4DD00059E
Linda Hung, Joyce A. Yager, Danielle Monteverde, Dave Baiocchi, Ha-Kyung Kwon, Shijing Sun and Santosh Suram
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引用次数: 0

摘要

研究人员在材料科学实验室自动化和自主化方面有哪些动机和挑战?我们就这一主题进行的调查收到了 102 份来自不同机构、担任不同职务的研究人员的回复。加速发现是回复中的一个明确主题,另一个主题是对人类研究人员角色的担忧。调查对象分享了各种以加速材料发现为目标的使用案例,包括部分自动化比完全自动驾驶实验室更受欢迎的例子。根据观察到的研究人员优先事项和需求模式,我们提出了从非自动化(L0)到完全自主(L5)的实验室自主水平框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Autonomous laboratories for accelerated materials discovery: a community survey and practical insights†

What are researchers' motivations and challenges related to automation and autonomy in materials science laboratories? Our survey on this topic received 102 responses from researchers across a variety of institutions and in a variety of roles. Accelerated discovery was a clear theme in the responses, and another theme was concern about the role of human researchers. Survey respondents shared a variety of use cases targeting accelerated materials discovery, including examples where partial automation is preferred over full self-driving laboratories. Building on the observed patterns of researcher priorities and needs, we propose a framework for levels of laboratory autonomy from non-automated (L0) to fully autonomous (L5).

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Back cover ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
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