Swarm Intelligence for Chemical Reaction Optimisation
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866908539439546368 |
|---|---|
| author | Schlama, Rémi Sin, Joshua W. Burwood, Ryan P. Püntener, Kurt Bigler, Raphael Schwaller, Philippe |
| author_facet | Schlama, Rémi Sin, Joshua W. Burwood, Ryan P. Püntener, Kurt Bigler, Raphael Schwaller, Philippe |
| contents | Chemical reaction optimisation is essential for synthetic chemistry and pharmaceutical development, demanding the extensive exploration of many reaction parameters to achieve efficient and sustainable processes. We report $α$-PSO, a novel nature-inspired metaheuristic algorithm that augments canonical particle swarm optimisation (PSO) with machine learning (ML) for parallel reaction optimisation. Unlike black-box ML approaches that obscure decision-making processes, $α$-PSO uses mechanistically clear optimisation strategies through simple, physically intuitive swarm dynamics directly connected to experimental observables, enabling practitioners to understand the components driving each optimisation decision. We establish a theoretical framework for reaction landscape analysis using local Lipschitz constants to quantify reaction space "roughness", distinguishing between smoothly varying landscapes with predictable surfaces and rough landscapes with many reactivity cliffs. This analysis guides adaptive $α$-PSO parameter selection, optimising performance for different reaction topologies. Systematic evaluation of $α$-PSO across pharmaceutically relevant reaction benchmarks demonstrates competitive performance with state-of-the-art Bayesian optimisation methods, while two prospective high-throughput experimentation (HTE) campaigns showed that $α$-PSO identified optimal reaction conditions more rapidly than Bayesian optimisation. $α$-PSO combines the predictive capability of advanced black-box ML methods with interpretable metaheuristic procedures, offering chemists an effective framework for parallel reaction optimisation that maintains methodological clarity while achieving highly performant experimental outcomes. Alongside our open-source $α$-PSO implementation, we release $989$ new high-quality Pd-catalysed Buchwald-Hartwig and Suzuki reactions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11798 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Swarm Intelligence for Chemical Reaction Optimisation Schlama, Rémi Sin, Joshua W. Burwood, Ryan P. Püntener, Kurt Bigler, Raphael Schwaller, Philippe Chemical Physics Chemical reaction optimisation is essential for synthetic chemistry and pharmaceutical development, demanding the extensive exploration of many reaction parameters to achieve efficient and sustainable processes. We report $α$-PSO, a novel nature-inspired metaheuristic algorithm that augments canonical particle swarm optimisation (PSO) with machine learning (ML) for parallel reaction optimisation. Unlike black-box ML approaches that obscure decision-making processes, $α$-PSO uses mechanistically clear optimisation strategies through simple, physically intuitive swarm dynamics directly connected to experimental observables, enabling practitioners to understand the components driving each optimisation decision. We establish a theoretical framework for reaction landscape analysis using local Lipschitz constants to quantify reaction space "roughness", distinguishing between smoothly varying landscapes with predictable surfaces and rough landscapes with many reactivity cliffs. This analysis guides adaptive $α$-PSO parameter selection, optimising performance for different reaction topologies. Systematic evaluation of $α$-PSO across pharmaceutically relevant reaction benchmarks demonstrates competitive performance with state-of-the-art Bayesian optimisation methods, while two prospective high-throughput experimentation (HTE) campaigns showed that $α$-PSO identified optimal reaction conditions more rapidly than Bayesian optimisation. $α$-PSO combines the predictive capability of advanced black-box ML methods with interpretable metaheuristic procedures, offering chemists an effective framework for parallel reaction optimisation that maintains methodological clarity while achieving highly performant experimental outcomes. Alongside our open-source $α$-PSO implementation, we release $989$ new high-quality Pd-catalysed Buchwald-Hartwig and Suzuki reactions. |
| title | Swarm Intelligence for Chemical Reaction Optimisation |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2509.11798 |