# CateCom: a practical data-centric approach to categorization of computational models

arXiv: https://arxiv.org/abs/2109.13452 · HTML: https://mlsanity.com/paper/2109.13452/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 64 references that are in the map.
- First posted on arXiv 28 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance](https://mlsanity.com/paper/2003.12476/) (2020)
- [AiiDA: Automated Interactive Infrastructure and Database for Computational Science](https://mlsanity.com/paper/1504.01163/) (2015)
- [An Ontology for the Materials Design Domain](https://mlsanity.com/paper/2006.07712/) (2020)
- [Accessible computational materials design with high fidelity and high throughput](https://mlsanity.com/paper/1807.05623/) (2018)
- [Data-centric online ecosystem for digital materials science](https://mlsanity.com/paper/1902.10838/) (2019)
- [Electronic properties of binary compounds with high fidelity and high throughput](https://mlsanity.com/paper/1808.05325/) (2018)
- [Fast and accessible first-principles calculations of vibrational properties of materials](https://mlsanity.com/paper/1808.10011/) (2018)

## Built on by (most cited first, 1 of 1)

- [AI-ready design of realistic 2D materials and interfaces with Mat3ra-2D](https://mlsanity.com/paper/2603.27886/) (2026)

## Abstract (arXiv)

The advent of data-driven science in the 21st century brought about the need for well-organized structured data and associated infrastructure able to facilitate the applications of Artificial Intelligence and Machine Learning. We present an effort aimed at organizing the diverse landscape of physics-based and data-driven computational models in order to facilitate the storage of associated information as structured data. We apply object-oriented design concepts and outline the foundations of an open-source collaborative framework that is: (1) capable of uniquely describing the approaches in structured data, (2) flexible enough to cover the majority of widely used models, and (3) utilizes collective intelligence through community contributions. We present example database schemas and corresponding data structures and explain how these are deployed in software at the time of this writing.