Prediction of the Phase Composition Profile of Three-Compound Mixtures in Liquid-Liquid Equilibrium: A Chemoinformatics Approach.
Chemphyschem
; 23(24): e202200300, 2022 12 16.
Article
en En
| MEDLINE
| ID: mdl-35929613
ABSTRACT
Machine-learning models were developed to predict the composition profile of a three-compound mixture in liquid-liquid equilibrium (LLE), given the global composition at certain temperature and pressure. A chemoinformatics approach was explored, based on the MOLMAP technology to encode molecules and mixtures. The chemical systems involved an ionic liquid (IL) and two organic molecules. Two complementary models have been optimized for the IL-rich and IL-poor phases. The two global optimized models are highly accurate, and were validated with independent test sets, where combinations of molecule1+molecule2+IL are different from those in the training set. These results highlight the MOLMAP encoding scheme, based on atomic properties to train models that learn relationships between features of complex multi-component chemical systems and their profile of phase compositions.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Líquidos Iónicos
/
Quimioinformática
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Chemphyschem
Asunto de la revista:
BIOFISICA
/
QUIMICA
Año:
2022
Tipo del documento:
Article
País de afiliación:
Portugal