Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Artificial Neural Networks (ANNs) are transforming how
we understand
chemical mixtures, providing an expressive view of the chemical space
and multiscale processes. Their hybridization with physical knowledge
can bridge the gap between predictivity and understanding of the underlying
processes. This overview explores recent progress in ANNs, particularly
their potential in the ’recomposition’ of chemical mixtures.
Graph-based representations reveal patterns among mixture components,
and deep learning models excel in capturing complexity and symmetries
when compared to traditional Quantitative Structure–Property
Relationship models. Key components, such as Hamiltonian networks
and convolution operations, play a central role in representing multiscale
mixtures. The integration of ANNs with Chemical Reaction Networks
and Physics-Informed Neural Networks for inverse chemical kinetic
problems is also examined. The combination of sensors with ANNs shows
promise in optical and biomimetic applications. A common ground is
identified in the context of statistical physics, where ANN-based
methods iteratively adapt their models by blending their initial states
with training data. The concept of mixture recomposition unveils a
reciprocal inspiration between ANNs and reactive mixtures, highlighting
learning behaviors influenced by the training environment.