Quantitative solvent selection toolkits for functional materials preparation Xue Fang, Ulzhalgas Karatayeva, Natalie Fey, Ella Gale, Charl FJ Faul School of Chemistry, University of Bristol, UK Solvents are critically important for the successful completion of a range of chemical processes, including synthesis, functionality regulation, amongst other. 1 Nevertheless, optimising solvent systems via a bottom- up approach is challenging. Conventional solvent classification (apolar, polar protic and polar aprotic) can be oversimplified to describe the delicate solvent-solute interaction. Developing a cost-effective quantitative solvent selection method is therefore an urgent need. With the support of data science and machine learning, we hereby report two open-source Python toolkits, SolvPred and MLoc 2, 3 , where MLoc applies an analogous design inspired by the K-means clustering approach to quantify the solvent-material compatibility; SolvPred predicts multi- solvent alternatives to approximate target solvent properties. Both toolkits are based on the deconvolution of molecular interactions achieved by the Hansen solubility parameters (HSPs) 4 , allowing researchers to understand the contribution of dispersion, dipolar and H-bonding interaction involved in the chemical process of interests. We demonstrate the applications of these toolkits to optimise CO 2 uptake efficiency of porous materials and to analyse corresponding mechanisms. These toolkits offer a novel strategy for efficient property control of functional materials via rational solvent selection. In future work, we will integrate these toolkits with high throughput synthesis platform for data-driven design of functional materials. References 1. a) J. Chen, W. Yan, E. J. Townsend, J. Feng, L. Pan, V. Del Angel Hernandez and C. F. J. Faul, Angew. Chem. Int. Ed. , 2019, 58 , 11715–11719. b) J. Chen, T. Qiu, W. Yan and C. F. J. Faul, J. Mater. Chem. A , 2020, 8 , 22657–22665. 2. a) X. Fang, N. Fey, E. Gale, C. F.J. Faul, HSP Toolkits – A General Introduction (Github), https://github.com/xueannafang/ HSP_toolkit_docs, 2023. b) X. Fang, N. Fey, E. Gale, C. F.J. Faul, SolvPred v2.0 (Github), https://github.com/xueannafang/ hsp_toolkit_solv_pred_v_2.0, 2023. c) X. Fang, N. Fey, E. Gale, C. F.J. Faul, MLoc v2.0 (Github), https://github.com/ xueannafang/hsp_mloc_v2, 2023. 3. a) X. Fang, N. Fey, E. Gale, C. F.J. Faul, SolvPred – A multi-solvent prediction toolkit for target Hansen solubility parameters approximation (manuscript in preparation) , 2023. b) X. Fang, U. Karatayeva, N. Fey, E. Gale, C. F.J. Faul, MLoc - A quantitative solvent selection toolkit for porous materials preparation (manuscript in preparation) , 2023. 4. C. Hansen, Hansen Solubility Parameters – A user’s handbook, 2 nd edition, 2011.
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