@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.},
	language = {en},
	number = {1},
	urldate = {2025-08-14},
	journal = {Nature Communications},
	author = {Sin, Joshua W. and Chau, Siu Lun and Burwood, Ryan P. and Püntener, Kurt and Bigler, Raphael and Schwaller, Philippe},
	month = jul,
	year = {2025},
	pages = {6464},
}