结束语:关于化学科学中数据驱动的发现的法拉第讨论。

IF 3.3 3区 化学 Q2 CHEMISTRY, PHYSICAL Faraday Discussions Pub Date : 2024-11-22 DOI:10.1039/d4fd00174e
Andrew I Cooper
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引用次数: 0

摘要

本次法拉第讨论会首次关注大数据、机器学习和人工智能在化学科学中日益重要的作用。其目的是对这些主题进行批判性讨论,并探讨数据如何在现在和未来促成化学新发现的问题。该计划横跨计算和实验工作,涵盖自然语言处理、机器学习潜能、优化策略、机器人和自动驾驶实验室等新兴课题。在此,我将简要介绍这一领域的历史,并就所涉及的讨论主题发表一些个人看法,最后提出这一领域未来面临的三个挑战。
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Concluding remarks: Faraday Discussion on data-driven discovery in the chemical sciences.

This Faraday Discussion was the first to focus on the increasingly central role of big data, machine learning, and artificial intelligence in the chemical sciences. The aim was to critically discuss these topics, and to explore the question of how data can enable new discoveries in chemistry, both now and in the future. The programme spanned computational and experimental work, and encompassed emerging topics such as natural language processing, machine-learned potentials, optimization strategies, and robotics and self-driving laboratories. Here I provide some brief introductory comments on the history of this field, along with some personal views on the discussion topics covered, concluding with three future challenges for this area.

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来源期刊
Faraday Discussions
Faraday Discussions 化学-物理化学
自引率
0.00%
发文量
259
期刊介绍: Discussion summary and research papers from discussion meetings that focus on rapidly developing areas of physical chemistry and its interfaces
期刊最新文献
Concluding remarks: Faraday Discussion on data-driven discovery in the chemical sciences. Spiers Memorial Lecture: New horizons in nanoelectrochemistry. Concluding remarks: Faraday Discussion on NMR crystallography. Analysis of uncertainty of neural fingerprint-based models. Metastable layered lithium-rich niobium and tantalum oxides via nearly instantaneous cation exchange.
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