Predicting self-assembly: from empirism to determinism.

Area of Science:

• Material Science
• Supramolecular Chemistry
• Computational Chemistry

Background:

• Self-assembly is crucial in modern science, yet predicting its outcomes in material science remains challenging.
• Rational molecular design is well-established for biological and pharmaceutical uses but less so for materials.
• Existing efforts to formally refine self-assembly predictions in material science are limited.

Purpose of the Study:

• To systematically review significant efforts in understanding thermodynamically stable self-assembly.
• To provide a framework for correlating chemical design with self-assembly predictions.
• To advance quantitative predictions and *ab initio* molecular design for self-assembling materials.

Main Methods:

• Discussion of experimental and computer-simulated self-assembly events.
• Application of partition functions as a unifying theoretical framework.
• Analysis across hard-matter, soft-matter, and higher symmetry architectures.

Main Results:

• Demonstration of partition functions as a common framework for studying self-assembly.
• Correlation established between chemical design/engineering and partition function landscapes.
• Progress towards understanding and predicting reversible self-assembly processes.

Conclusions:

• The partition function framework offers a pathway to quantitative predictions in self-assembly.
• Integrating molecular design with partition function knowledge is key for *ab initio* material design.
• This approach facilitates the rational engineering of self-assembling materials.