The ability to guarantee a single homogeneous liquid phase is a key consideration in computer-aided mixture/blend design (CAMbD). In this article, we investigate the use of a classifier surrogate of the phase stability condition within a CAMbD optimisation model for designing solvent mixtures with guaranteed phase stability properties. We show how to develop such classifiers for describing multiple candidate mixtures over a range of compositions and temperatures based on the generation of phase stability data using thermodynamic models such as UNIFAC. We test the approach on two solvent design case studies and illustrate its effectiveness in enabling the in silico design of stable mixtures, simultaneously providing a probability of phase stability as an interpretable metric.

Asserting phase stability entails the global solution of a nonconvex optimisation problem, typically the tangent plane distance minimisation (TPDM). To improve computational tractability, we propose classifier-based surrogate models to replace the TPDM. We seek models that represent several multicomponent mixtures simultaneously, across various component identities, temperatures, and compositions. We investigate both artificial neural networks (ANN) and support vector machines (SVM) and use Matthew's correlation coefficient (MCC) as performance metric for the corresponding binary classification problems. For SVM models, linear, polynomial, and radial basis function (RBF) kernels are assessed; while for ANNs, the tanh and relu activation functions are investigated. We test the performance of these surrogate models on a set of ternary mixtures that involve ibuprofen and two solvents with fixed or variable temperatures. The results show that ANNs and SVMs can both predict phase stability reliably, with RBF-SVM giving the lowest computational cost.