The Cronin Group

Research in the Cronin Group is motivated by the fascination for complex chemical systems, and the desire to construct complex functional molecular architectures that are not based on biologically derived building blocks.


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Robots VS Humans in the search for chemical intelligence

A new paper has just been published by the Cronin group that shows how machine learning can be used to search for new molecules and crystals of those molecules more efficiently that either a human experimenter or a random algorithm. This paper outlines part of the Cronin Group’s digital chemistry vision to help human experimenters enhance their work by using machine learning and artificial intelligence combined with chemical search systems.

News articles featuring this work have been published in Chemistry World and at ChemEurope.com.

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Prof. Leroy (Lee) Cronin

Prof Leroy (Lee) Cronin
Regius Chair of Chemistry
Cronin Laboratory
School of Chemistry
Joseph Black Building
University of Glasgow
Glasgow G12 8QQ
Tel: +44 141 330 6650
Email: lee.cronin@glasgow.ac.uk

Latest Publications

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452. [Fe15]: a frustrated, centred tetrakis hexahedron

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451. Robotic Stepwise Synthesis of Hetero-Multinuclear Metal Oxo Clusters as Single-Molecule Magnets

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450. Standardization and Control of Grignard Reactions in a Universal Chemical Synthesis Machine using online NMR

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449. A robotic prebiotic chemist probes long term reactions of complexifying mixtures

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448. Identifying molecules as biosignatures with assembly theory and mass spectrometry

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447. A molecular computing approach to solving optimization problems via programmable microdroplet arrays

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446. Influence of the Contact Geometry and Counterions on the Current Flow and Charge Transfer in Polyoxometalate Molecular Junctions: A Density Functional Theory Study

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445. Elucidating the Paramagnetic Interactions of an Inorganic-Organic Hybrid Radical-Functionalized Mn-Anderson Cluster

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444. Automatic Generation of 3D-Printed Reactionware for Chemical Synthesis Digitization using ChemSCAD

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443. Optimization of Formulations Using Robotic Experiments Driven by Machine Learning DoE


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