Similarity-Informed Matrix Completion Method for Predicting Activity Coefficients

Accurate prediction of thermodynamic properties of mixtures,
such
as activity coefficients, is essential for designing and optimizing
chemical processes. While established physics-based methods face limitations
in prediction accuracy and scope, emerging machine learning approaches,
such as matrix completion methods (MCMs), offer promising alternatives.
However, their performance can suffer in data-sparse regions. To address
this issue, we propose a novel hybrid MCM for predicting activity
coefficients at infinite dilution at 298 K that not only uses experimental
training data but also includes synthetic training data from two sources:
predictions obtained from the physics-based modified UNIFAC (Dortmund)
and from a similarity-based approach developed in previous work. The
resulting hybrid method combines the broad applicability of MCMs with
the precision of the similarity-based approach, resulting in a more
robust prediction framework that excels even in regions with limited
data. Additionally, our analysis provides valuable insights into how
different types of training data affect the prediction accuracy. When
experimental data are sparse, incorporating synthetic training data
from modified UNIFAC (Dortmund) and the similarity-based approach
significantly improves the performance of the MCMs. Conversely, even
with abundant experimental data, high accuracy is achieved only if
the training set includes mixtures similar to those of interest.