@article{sin_highly_2025,
	title = {Highly parallel optimisation of chemical reactions through automation and machine intelligence},
	volume = {16},
	issn = {2041-1723},
	url = {https://www.nature.com/articles/s41467-025-61803-0},
	doi = {10.1038/s41467-025-61803-0},
	abstract = {Abstract 
            We report the development and application of a scalable machine learning ({ML}) framework (Minerva) for highly parallel multi-objective reaction optimisation with automated high-throughput experimentation ({HTE}). Minerva demonstrates robust performance with experimental data-derived benchmarks, efficiently handling large parallel batches, high-dimensional search spaces, reaction noise, and batch constraints present in real-world laboratories. Validating our approach experimentally, we apply Minerva in a 96-well {HTE} reaction optimisation campaign for a nickel-catalysed Suzuki reaction, tackling challenges in non-precious metal catalysis. Our approach effectively navigates the complex reaction landscape with unexpected chemical reactivity, outperforming traditional experimentalist-driven methods. Extending to industrial applications, we deploy Minerva in pharmaceutical process development, successfully optimising two active pharmaceutical ingredient ({API}) syntheses. For both a Ni-catalysed Suzuki coupling and a Pd-catalysed Buchwald-Hartwig reaction, our approach identifies multiple conditions achieving {\textgreater}95 area percent ({AP}) yield and selectivity, directly translating to improved process conditions at scale.},
	pages = {6464},
	number = {1},
	journaltitle = {Nature Communications},
	shortjournal = {Nat Commun},
	author = {Sin, Joshua W. and Chau, Siu Lun and Burwood, Ryan P. and Püntener, Kurt and Bigler, Raphael and Schwaller, Philippe},
	urldate = {2025-08-14},
	date = {2025-07-12},
	langid = {english},
}