Materials Cloud, a platform for open computational science

Abstract: 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 the FAIR principles that extends to computational workflows. Materials Cloud leverages the AiiDA framework to record the provenance of entire simulation pipelines (calculations performed, codes used, data generated) in the form of graphs that allow to retrace and reproduce 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.

• The paper introduces Materials Cloud as an integrated platform that leverages the AiiDA workflow manager to record complete simulation data and ensure reproducibility.
• Key features include persistent archival of raw and processed data, adherence to FAIR principles, and support for diverse simulation codes.
• Implications extend to enhancing data accessibility and promoting open science, which advances transparent, repeatable research in computational materials science.

An Overview of Materials Cloud: A Platform for Open Computational Science

The paper, "Materials Cloud, a platform for open computational science," presents the development and implementation of Materials Cloud, an online platform designed to facilitate open science in computational materials science. This platform aims to support the dissemination and reproducibility of research outputs by adhering to the FAIR principles - making data Findable, Accessible, Interoperable, and Reusable. By incorporating the AiiDA framework, Materials Cloud effectively manages the entire data lifecycle, enhancing transparency and reproducibility in scientific research.

Core Features and Contributions

Materials Cloud is structured around several core functionalities that together support a comprehensive open science framework:

1. Archival and Dissemination: The platform provides services for archiving and disseminating both raw and curated data, along with their provenance graphs. This ensures that the data is citable, persistently archived, and adheres to the FAIR guidelines. Notably, by using AiiDA, the provenance of entire simulation pipelines is recorded, making it feasible to retrace and reproduce computational results.
2. Support for Diverse Computing Codes: The infrastructure is agnostic to specific simulation codes, supporting various computational applications beyond materials modeling. This broad applicability indicates potential benefits across multiple fields of computational science.
3. Integration with AiiDA: A key aspect of Materials Cloud is its deep integration with AiiDA, a workflow manager that emphasizes data provenance, performance, and extensibility. AiiDA operates as both a simulation manager and a data tracker, which records the full data trail from input to output across computational workflows. The design promotes reproducibility out-of-the-box.
4. Provision of Turnkey Solutions: The platform offers turnkey solutions which allow users to perform standardized computational tasks easily. This helps in catering to diverse user bases, extending from computational scientists to industrial researchers and beyond.
5. Interactive Tools and Educational Resources: Through interfaces like DISCOVER and EXPLORE, Materials Cloud offers tools designed for simulation execution, data analytics, and education, including resources such as video tutorials and virtual machines for hands-on learning.

Implications and Future Prospects

The implications of Materials Cloud extend both practically and theoretically in the domain of computational science. By promoting open access and reproducibility, Materials Cloud helps address some of the long-standing challenges in scientific research regarding data transparency and validation. The use of AiiDA for tracking and managing computational tasks allows researchers to focus more on discovery and innovation as opposed to infrastructure concerns.

In terms of future developments, the platform represents a step toward more interconnected scientific infrastructure, where the sharing of standard protocols and formats plays a crucial role in advancing collective scientific understanding. As efforts converge on creating interoperable data standards, platforms like Materials Cloud could serve as pivotal hubs for integrating diverse datasets and computational results. The authors also note the potential for expanding Materials Cloud's capabilities in line with evolving technological and scientific needs, thereby broadening its impact on the research community.

In conclusion, Materials Cloud exemplifies a well-structured approach to open computational science, embodying an innovative use of technological frameworks to enhance the accessibility, transparency, and reproducibility of research outputs. Its ongoing development and increasing integration within the scientific community suggest it will remain a vital resource for the advancement of open science principles in computational research.