Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview.

Area of Science:

• Chemistry
• Computer Science
• Physics

Background:

• Artificial Neural Networks (ANNs) offer advanced capabilities for analyzing complex chemical systems.
• Integrating ANNs with physical principles is crucial for bridging predictive power and mechanistic understanding.
• Traditional Quantitative Structure-Property Relationship (QSPR) models have limitations in capturing the intricacies of chemical mixtures.

Purpose of the Study:

• To provide an overview of recent advancements in ANNs for chemical mixture analysis.
• To explore the potential of ANNs in the 'recomposition' of chemical mixtures.
• To highlight the integration of ANNs with physical knowledge and other computational methods.

Main Methods:

• Utilizing graph-based representations to identify patterns in mixture components.
• Employing deep learning models, including Hamiltonian networks and convolution operations, for complex data.
• Integrating ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks.

Main Results:

• ANNs demonstrate superior performance over traditional QSPR models in capturing mixture complexity and symmetries.
• Graph-based methods and deep learning effectively represent multiscale processes in chemical mixtures.
• Hybrid approaches show promise in areas like optical and biomimetic applications, and inverse chemical kinetic problems.

Conclusions:

• ANNs, particularly when hybridized with physical knowledge, significantly advance the understanding and prediction of chemical mixtures.
• The concept of 'mixture recomposition' using ANNs reveals a symbiotic relationship between AI and chemical systems.
• Future applications span diverse fields, driven by the adaptive learning capabilities of ANN-based methods grounded in statistical physics.