mlsanityData to 7 Oct 2026

MaterialsAtlas.org: A Materials Informatics Web App Platform for Materials Discovery and Survey of State-of-the-Art

arXiv 2109.04007 ↗First posted 9 Sep 2021cond-mat.mtrl-sci cs.LGcited by 5

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Abstract (arXiv)

The availability and easy access of large scale experimental and computational materials data have enabled the emergence of accelerated development of algorithms and models for materials property prediction, structure prediction, and generative design of materials. However, lack of user-friendly materials informatics web servers has severely constrained the wide adoption of such tools in the daily practice of materials screening, tinkering, and design space exploration by materials scientists. Herein we first survey current materials informatics web apps and then propose and develop MaterialsAtlas.org, a web based materials informatics toolbox for materials discovery, which includes a variety of routinely needed tools for exploratory materials discovery, including materials composition and structure check (e.g. for neutrality, electronegativity balance, dynamic stability, Pauling rules), materials property prediction (e.g. band gap, elastic moduli, hardness, thermal conductivity), and search for hypothetical materials. These user-friendly tools can be freely accessed at urlwww.materialsatlas.org. We argue that such materials informatics apps should be widely developed by the community to speed up the materials discovery processes.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 128 references that are in the map. First posted on arXiv 9 Sep 2021.

Builds on

21 of 128 references.

References · 21

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9 more
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Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 2.

5 papers.

  1. 2024Structure-based out-of-distribution (OOD) materials property prediction: a benchmark studycited by 17
  2. 2022Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materialscited by 8
  3. 2023Challenges and opportunities for machine learning in multiscale computational modelingcited by 6
  4. 2025Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterizationcited by 0
  5. 2025Reliable End-to-End Material Information Extraction from the Literature with Source-Tracked Multi-Stage Large Language Modelscited by 0