Issue 38, 2023

The rise of automated curiosity-driven discoveries in chemistry

Abstract

The quest for generating novel chemistry knowledge is critical in scientific advancement, and machine learning (ML) has emerged as an asset in this pursuit. Through interpolation among learned patterns, ML can tackle tasks that were previously deemed demanding to machines. This distinctive capacity of ML provides invaluable aid to bench chemists in their daily work. However, current ML tools are typically designed to prioritize experiments with the highest likelihood of success, i.e., higher predictive confidence. In this perspective, we build on current trends that suggest a future in which ML could be just as beneficial in exploring uncharted search spaces through simulated curiosity. We discuss how low and ‘negative’ data can catalyse one-/few-shot learning, and how the broader use of curious ML and novelty detection algorithms can propel the next wave of chemical discoveries. We anticipate that ML for curiosity-driven research will help the community overcome potentially biased assumptions and uncover unexpected findings in the chemical sciences at an accelerated pace.

Graphical abstract: The rise of automated curiosity-driven discoveries in chemistry

Article information

Article type
Perspective
Submitted
02 jul 2023
Accepted
07 set 2023
First published
08 set 2023
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2023,14, 10378-10384

The rise of automated curiosity-driven discoveries in chemistry

L. Bustillo, T. Laino and T. Rodrigues, Chem. Sci., 2023, 14, 10378 DOI: 10.1039/D3SC03367H

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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