With the advent of the AI4S era, concurrent learning, high-throughput computing, and distributed and heterogeneous infrastructure poke the pain points of the traditional workflow solutions. dflow aims to provide all scientists and engineers with scientific computing workflow. dflow is a cloud-native scientific computing workflow framework based on the Argo workflow engine that can be geared towards the needs and usage habits of the scientific computing community, combining the characteristics of AI, computing resources, and scientific computing.
Here, dflow101 project hopes to encourage increasingly more scientific work using this kind of advanced workflow.
In dflow repo, there are several documents for your reference to help you get started quickly.
- dflow for VASP single-point calculations (ELF, DOS, charge density) using pymatgen, LINK
We welcome anyone interested to join dflow101. The expected dflow project can be released in your own Github repo, including a README document, executable python scripts and supporting files (e.g. uploaded files, Dockerfile and others).
For more information, please refer to submission_guidelines.md here.
Come on and join this fantastic project!