The Evolving Quest for Chemical Understanding in the Quantum Age

Area of Science:

• Theoretical and computational chemistry.
• Quantum mechanics and its evolving role.

Background:

• Traditional methods like valence bond theory, molecular orbital theory, and density functional theory have long formed the field's foundation.
• The emergence of machine learning (ML) and quantum computing (QC) presents new paradigms for chemical representation and understanding.

Purpose of the Study:

• To examine the evolution of chemical understanding over the past century.
• To explore how emergent chemical concepts link theories to understanding.
• To propose a general scheme for extracting chemical understanding from fundamental theories.

Main Methods:

• Analysis through the lenses of ontology, epistemology, and emergence.
• Development of a scheme to derive chemical understanding from basic theoretical variables.
• Extending the scheme to deep learning and quantum computing.

Main Results:

• Chemical concepts (e.g., aromaticity, reactivity) are emergent, not merely reducible to physics.
• Hierarchical modeling is proposed as a new platform for integrating abstraction and capturing emergent behaviors.
• Roadmaps for harvesting chemical understanding from ML and QC are suggested.

Conclusions:

• The future of chemical understanding requires an epistemological shift, embracing conceptual pluralism and adaptability.
• Deeper appreciation of the multilayered ontological structure of molecular systems is crucial.
• Hierarchical modeling offers a path beyond traditional multiscale modeling for conceptual innovation.