The integration of machine learning and physical modeling is changing the paradigm of scientific research. Those who hope to extend the frontier of science and solve challenging practical problems through computational modeling are coming together in new ways never seen before. This calls for a new infrastructure--new platforms for collaboration, new coding frameworks, new data processing schemes, and new ways of using the computing power. It also calls for a new culture—the culture of working together closely for the benefit of all, of free exchange and sharing of knowledge and tools, of respect and appreciation of each other's work, and of the pursuit of harmony among diversity.
The DeepModeling community is a community of such a group of people.
If you want to contribute to an existing project in the DeepModeling community, please just do so or contact the corresponding developer directly; if you want to open a new project in the DeepModeling community, or if you want the DeepModeling community to help develop your project, just contact [email protected].
If you are a programmer who loves science and is attracted by the future scientific computing platform built by the DeepModeling community, you can contribute not only through new algorithms, but also code development specifications, document writing specifications, community databases, task scheduling, workflow management and other tools. In addition, you can contribute to code architecture design and high-performance optimization tasks in the DeepModeling community. People in the field of scientific computing will greatly appreciate your expertise and contribution.
If you are a hardcore developer familiar with topics such as electronic structure calculations, molecular dynamics, and finite element methods, the DeepModeling community will be your place to showcase your talents. The addition of machine learning components requires us to rethink about architecture design, each specific implementation for the tasks mentioned above and high-performance optimization. You will become important bridges that connect other developers, contributors, and users in different areas.
If you have only used some basic scientific software and have worked on some post-processing scripts, the DeepModeling community also needs you. Try to ask questions and communicate on github/gitee and other communication platforms, try to give opinions, and try to fork, commit, pr... Your little by little contribution will make the DeepModeling community better and better, and the DeepModeling community will be very grateful for such contributions.
Even if you are just a bystander, if you support the concept of the DeepModeling community, your recognition and dissemination will also be a great encouragement and support for the DeepModeling community.
Governing Board (GB) is responsible for the fundamental rules of the DeepModeling community. The Governing Board does not make technical decisions for the DeepModeling Community, except for working with the TOC to establish the overall scope.
Technical Oversight Committee (TOC) serves as an essential bridge and channel for integrating and sharing information across independent individuals, companies, and organizations. It is the coordination centre for problem-solving in terms of resource mobilization, technical research, and development outlook of the current community and cooperative projects.
Teams are groups that focus on individual parts of the DeepModeling projects. A team has its reviewer, committer, maintainer, and owns one or more repositories. Maintainers are responsible for team-level decision making.
The Technical Oversight Committee (TOC) is in charge of team creation, retirement, and arbitrates divergence between teams.
Despite the tremendous advances in AI and computing power, the scientific computing community is largely embedded in an old-fashioned culture. Many of the most important tasks rely on legacy codes. The core algorithms used in many commercial software have been outdated. The self-sufficient style of work is similar to that of the agricultural ages resulting in poor efficiency. It is only in recent years that some promising open-source communities have emerged. However, these communities are often aimed at specific tools for specific scales, and are often maintained by specific academic research groups. They face serious challenges in terms of continuous development and improved user experience.
The DeepModeling project promises to change all that.
The combination of machine learning and physical modeling calls for a new paradigm, the open-source community paradigm. Such a paradigm has long been embraced in the computer and electronics industry, with Linux and Andriod being the very well-known examples. In this sense, what the DeepModeling project does is to borrow these ideas and use them for scientific computing. For people in computational science and engineering, efficient and reusable modeling tools that can be continuously improved will free researchers from the plight of no model or with only ad hoc models. For those who work on machine learning, the world of physical models will provide a relatively new and surely vast playground. Working together as an open-source community will make our work more productive, up to date, reliable, and transparent. The spirit of close collaboration, of respect and building on each other’s work will surely inspire more and more people to join the cause of advancing computing for the benefit of the human society. This is an exciting opportunity. This is the future of scientific computing!
