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CateCom: A Practical Data-Centric Approach to Categorization of Computational Models.

Alexander Zech¹, Timur Bazhirov²

¹Kenneth S. Pitzer Center for Theoretical Chemistry, Department of Chemistry, University of California, Berkeley, California 94720, United States.

Journal of Chemical Information and Modeling

|March 1, 2022

Summary

This summary is machine-generated.

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This study introduces a framework for organizing computational models in data-driven science. It enables structured data storage for physics-based and AI/ML models, fostering collaboration and efficient data management.

Area of Science:

Computational Science
Data Science
Artificial Intelligence

Background:

21st-century data-driven science requires organized structured data and infrastructure.
Existing computational models (physics-based and data-driven) lack unified data organization.
Artificial intelligence (AI) and machine learning (ML) necessitate standardized data formats.

Purpose of the Study:

To organize the diverse landscape of computational models for structured data storage.
To develop a flexible, open-source framework for describing various models.
To leverage community contributions for collective intelligence in data management.

Main Methods:

Application of object-oriented design principles.
Development of an open-source collaborative framework.

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Creation of example database schemas and data structures.

Main Results:

A framework capable of uniquely describing computational approaches in structured data.
Flexibility to accommodate a majority of widely used models.
A system designed for community contributions and collective intelligence.

Conclusions:

The proposed framework facilitates structured data storage for computational models.
The open-source approach promotes collaboration and broad model coverage.
This infrastructure is crucial for advancing AI and ML in data-driven science.