Materials Cloud, a platform for open computational science

By Leopold Talirz, Snehal Kumbhar, Elsa Passaro, Aliaksandr V. Yakutovich, Valeria Granata, Fernando Gargiulo, Marco Borelli, M. Uhrin, Sebastiaan P. Huber, Spyros Zoupanos and 10 more. Scientific Data, 2020

393 citations recorded by the source corpus.

Materials Cloud is a platform designed to enable open and seamless sharing of resources for computational science, driven by applications in materials modelling. It hosts (1) archival and dissemination services for raw and curated data, together with their provenance graph, (2) modelling services and virtual machines, (3) tools for data analytics, and pre-/post-processing, and (4) educational materials. Data is citable and archived persistently, providing a comprehensive embodiment of entire simulation pipelines (calculations performed, codes used, data generated) in the form of graphs that allow retracing and reproducing any computed result. When an AiiDA database is shared on Materials Cloud, peers can browse the interconnected record of simulations, download individual files or the full database, and start their research from the results of the original authors. The infrastructure is agnostic to the specific simulation codes used and can support diverse applications in computational science that transcend its initial materials domain.

Subfields: Catalysis, Information Systems and Management, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/8958efba7a02e3653f27c0e759882b2f3352e896

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Catalysis

1. Xianglin Liu
2. Zongrui Pei
3. Jiaxin Zhang

In Materials Chemistry

1. Kevin Maik Jablonka
2. Seyed Mohamad Moosavi
3. Berend Smit
4. Xianglin Liu
5. Zongrui Pei
6. Jiaxin Zhang

Citations in the corpus

• Recent advances and applications of deep learning methods in materials science (2021, neutral): “org/home#statistics [88] CoreMOF 163k https://mof.”
• Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities (2022, background): “Another European DFT database is the Automated Interactive Infrastructure and Database for Computational Science (AiiDA) which can be accessed by its interactive platform, Materials cloud [112].”
• The Role of Machine Learning in the Understanding and Design of Materials (2020, background): “Recent development of infrastructures in this area, such as Materials Cloud and AiiDA, 35,146 − 148 and FireWorks 149 are opening promising paths toward addressing these issues.”