Bayesian optimisation for additive screening and yield improvements – beyond one-hot encoding

Ranković, Bojana; Griffiths, Ryan-Rhys; Moss, Henry B.; Schwaller, Philippe

doi:10.1039/d3dd00096f

Cited by 2 publications

(2 citation statements)

References 93 publications

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“…However, the validity of the model in the neighbourhood chemical space of molecules present in the dataset, will depend on the continuity and smoothness of the representation function in the chemical space. Identification of such appropriate descriptors requires exploring 58 , 59 a broad range machine learned and cheminformatics based representations 60 , 61 in combination with a wide variety of predictive classical 62 , 63 and machine learning models 57 , 64 – 68 and performing exhaustive testing. Molecular representations used by these models from the provided SMILES strings or after SMILES those to other datatypes like InChi, atomic graphs, or atomic position-based descriptions using cheminformatics tools like RDKit.…”

Section: Usage Notesmentioning

confidence: 99%

CALiSol-23: Experimental electrolyte conductivity data for various Li-salts and solvent combinations

de Blasio,

Elsborg,

Vegge

et al. 2024

Sci Data

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Ion transport in non-aqueous electrolytes is crucial for high performance lithium-ion battery (LIB) development. The design of superior electrolytes requires extensive experimentation across the compositional space. To support data driven accelerated electrolyte discovery efforts, we curated and analyzed a large dataset covering a wide range of experimentally recorded ionic conductivities for various combinations of lithium salts, solvents, concentrations, and temperatures. The dataset is named as ’Conductivity Atlas for Lithium salts and Solvents’ (CALiSol-23). Comprehensive datasets are lacking but are critical to building chemistry agnostic machine learning models for conductivity as well as data driven electrolyte optimization tasks. CALiSol-23 was derived from an exhaustive review of literature concerning experimental non-aqueous electrolyte conductivity measurement. The final dataset consists of 13,825 individual data points from 27 different experimental articles, in total covering 38 solvents, a broad temperature range, and 14 lithium salts. CALiSol-23 can help expedite machine learning model development that can help in understanding the complexities of ion transport and streamlining the optimization of non-aqueous electrolyte mixtures.

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Section: Usage Notesmentioning

confidence: 99%

CALiSol-23: Experimental electrolyte conductivity data for various Li-salts and solvent combinations

de Blasio,

Elsborg,

Vegge

et al. 2024

Sci Data

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show abstract

“…[1][2][3][4][5][6] Among the different ML frameworks, Bayesian optimization (BO) is ideally suited for this task. 7,8 Given some initial data, BO leverages predictions and their corresponding uncertainties to suggest the next most promising experiments to conduct. BO-driven reaction optimization has seen significant success in the last few years, especially in the automated laboratory and high-throughput experimentation (HTE) setting.…”

Section: Introductionmentioning

confidence: 99%

Cost-Informed Bayesian Reaction Optimization

Schoepfer,

Weinreich,

Laplaza

et al. 2024

Preprint

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Bayesian optimization (BO) is an increasingly popular method for optimization and development of chemical reactions. Although effective in guiding experimental design, BO does not account for experimentation costs: testing readily available reagents under different conditions might be more cost and time-effective than synthesizing or buying additional ones. To address this issue, we present cost-informed BO (CIBO), an approach tailored for the rational planning of chemical experimentation that prioritizes the most cost-effective experiments. Reagents are used only when their anticipated improvement in reaction performance sufficiently outweighs their costs. Our algorithm tracks the available reagents, including recently acquired ones, and dynamically updates their cost during the optimization. Using literature data of Pd-catalyzed reactions, we show that CIBO reduces the cost of reaction optimization by up to 90% compared to standard BO. Our approach is compatible with any type of cost, e.g., the cost of buying equipment or compounds, waiting time, and environmental or security concerns. We believe CIBO supersedes BO in chemistry and envision applications in both traditional and self-driving laboratories for experiment planning.

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Bayesian optimisation for additive screening and yield improvements – beyond one-hot encoding

Abstract: Cost-effective Bayesian optimisation screening of 720 additives on four complex reactions, achieving substantial yield improvements over baselines using chemical reaction representations beyond one-hot encoding.

Cited by 2 publications

References 93 publications

CALiSol-23: Experimental electrolyte conductivity data for various Li-salts and solvent combinations

CALiSol-23: Experimental electrolyte conductivity data for various Li-salts and solvent combinations

Cost-Informed Bayesian Reaction Optimization

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