Work

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

Year: 2024

Type: article

Abstract: 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.

Source: Faraday Discussions

DOI: 10.1039/d4fd00174e

Author: Andrew I. Cooper

Institution: University of Liverpool

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Leverhulme Trust, Royal Society, Leverhulme Research Centre for Functional Materials Design

Award: G137351841