scikit-matter : A Suite of Generalisable Machine Learning Methods Born out of Chemistry and Materials Science

Goscinski, Alexander; Principe, Victor Paul; Fraux, Guillaume; Kliavinek, Sergei; Helfrecht, Benjamin Aaron; Loche, Philip; Ceriotti, Michele; Cersonsky, Rose Kathleen

doi:10.12688/openreseurope.15789.2

Cited by 2 publications

(2 citation statements)

References 41 publications

(64 reference statements)

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“…The noncorrelated features T , molar ratio, and surface area of HBA and HBD are scaled individually using the same StandardFlexibleScaler but with the setting column_wise = True (normalize each column separately). We have used the StandardFlexibleScaler in Scikit-matter for feature scaling . Further, the model performance was evaluated using the statistical metric of regression coefficient ( R 2 ), average absolute relative deviation (AARD), and the root-mean-square-error (RMSE), and the best ML model was selected based on the lowest AARD and RMSE and highest R 2 values.…”

Section: Methodsmentioning

confidence: 99%

“…We have used the StandardFlexibleScaler in Scikitmatter for feature scaling. 56 Further, the model performance was evaluated using the statistical metric of regression coefficient (R 2 ), average absolute relative deviation (AARD), and the root-mean-square-error (RMSE), and the best ML model was selected based on the lowest AARD and RMSE and highest R 2 values.…”

Section: Calculation Of Cosmo-rs-derived Input Features Formentioning

confidence: 99%

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Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents

Mohan,

Jetti,

Smith

et al. 2024

J. Chem. Theory Comput.

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Deep eutectic solvents (DESs) are emerging as environmentally friendly designer solvents for mass transport and heat transfer processes in industrial applications; however, the lack of accurate tools to predict and thus control their viscosities under both a range of environmental factors and formulations hinders their general application. While DESs may serve as designer solvents, with nearly unlimited combinations, this unfortunately makes it experimentally infeasible to comprehensively measure the viscosities of all DESs of potential industrial interest. To assist in the design of DESs, we have developed several new machine learning (ML) models that accurately and rapidly predict the viscosities of a diverse group of DESs at different temperatures and molar ratios using, to date, one of the most comprehensive data sets containing the properties of over 670 DESs over a wide range of temperatures (278.15−385.25 K). Three ML models, including support vector regression (SVR), feed forward neural networks (FFNNs), and categorical boosting (CatBoost), were developed to predict DES viscosity as a function of temperature and molar ratio and contrasted with multilinear and two-factor polynomial regression baselines. Quantum chemistry-based, COSMO-RS-derived sigma profile (σ-profile) features were used as inputs for the ML models. The CatBoost model is excellent at externally predicting DES viscosity, as indicated by high R 2 (0.99) and low root-mean-square-error (RMSE) and average absolute relative deviations (AARD) (5.22%) values for the testing data sets, and 98% of the data points lie within the 15% of AARD deviations. Furthermore, SHapley additive explanation (SHAP) analysis was employed to interpret the ML results and rationalize the viscosity predictions. The result is an ML approach that accurately predicts viscosity and will aid in accelerating the design of appropriate DESs for industrial applications.

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Section: Methodsmentioning

confidence: 99%

Section: Calculation Of Cosmo-rs-derived Input Features Formentioning

confidence: 99%