"Visualize, Describe, Compare"─Nanoinformatics Approaches for Material-Omics.

Area of Science:

• Nanomaterials science
• Computational chemistry
• Data science

Background:

• Nanoinformatics, the computational analysis of nanomaterials, is an emerging field.
• Existing data mining methods are insufficient for complex, unstructured nanomaterial datasets, especially those lacking crystalline structure.
• Data-driven discovery necessitates advanced in silico tools for nanomaterial characterization.

Purpose of the Study:

• To present a novel computational framework for analyzing single-particle and single-molecule data from super-resolution microscopy.
• To enable visualization, quantitative description, and global comparison of nanoparticle datasets.
• To address the need for robust data analysis in nanoinformatics.

Main Methods:

• Development of a computational framework for multiparametric nanoparticle data visualization.
• Implementation of molecular descriptors for quantitative material evaluation.
• Application of the framework to compare diverse nanoparticle libraries.

Main Results:

• The framework successfully visualized complex nanoparticle datasets, revealing material properties and heterogeneity.
• Quantitative evaluation and global comparison of different nanomaterials were achieved.
• Correlations between synthesis parameters and physicochemical properties were identified.
• The approach uncovered hidden batch-to-batch variations in nanoparticle data.

Conclusions:

• The developed computational framework advances nanoinformatics by providing essential tools for analyzing complex nanomaterial data.
• This method facilitates a deeper understanding of nanoparticle heterogeneity, synthesis-property relationships, and data quality.
• The framework has the potential to improve nanomaterial design and quality control by revealing subtle variations.