Concluding remarks: <i>Faraday Discussion</i> on data-driven discovery in the chemical sciences

This <i>Faraday Discussion</i> 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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Publication Details

Journal
Faraday Discussions
Published
2024-11-04
DOI
https://doi.org/10.1039/d4fd00174e
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.12

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article

Concluding remarks: Faraday Discussion on data-driven discovery in the chemical sciences

Andrew I. Cooper
1 citations
Faraday Discussions
Machine Learning in Materials Science
0.12
article

Concluding remarks: Faraday Discussion on data-driven discovery in the chemical sciences

Andrew I. Cooper
article en
1 citations

Abstract

This <i>Faraday Discussion</i> 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.

Faraday DiscussionsVol. 256(0)
University of Liverpool (GB)
Leverhulme Trust, Leverhulme Research Centre for Functional Materials Design
Openalex Percentile: Top 58%
Machine Learning in Materials Science
0.12
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Concluding remarks: <i>Faraday Discussion</i> on data-driven discovery in the chemical sciences — Andrew I. Cooper · Faraday Discussions (2024) | TGRS Research Map | TGRS