People attracted by DeepModeling for its openness, inclusiveness, and dedication to advancing scientific computing worldwide.
The two most important applications of computing are machine learning and physical modeling. The former is an effective tool for analyzing complex data; the latter is a scientific description of the physical world. The vitality boosted by the effective integration of the two is changing all aspects of scientific research. DeepModeling will ultimately be a set of methodologies and tools that combine machine learning, physical modeling, and cutting-edge computational platforms.
Released DeePMD-kit
The "DeepModeling Community" started with the initiation of the "DeePMD-kit" project. "DeePMD-kit" is a software tool that combines machine learning and molecular dynamics, which helps to overcome a long-standing difficulty in the field of molecular dynamics, namely the dilemma of having to choose between efficiency and accuracy.
DeepModeling Community
The name "DeepModeling" was proposed by early developers of the deepmd-kit project, with the intention of using deep learning tools to solve the curse of dimensionality problem in multi-scale modeling. DeepModeling has therefore become the name of the GitHub organization which manages the original deepmd-kit project.
Global Launch of Product
After the development of deepmd-kit, the DeepModeling community has successively initiated projects such as dpdata, dp-gen, and dpdispatcher, and extended the modeling scale to electronic structure level through projects such as deepks-kit and ABACUS. These projects have brought together people from all over the world working on molecular simulations.
Community Vision
In the short term, developers in the DeepModeling community will focus on atomic-scale simulation methods and tools. This includes solving the many-body Schrödinger equation, electronic structure calculation, molecular dynamics simulation, and coarse-grained molecular dynamics simulation. This also includes tasks such as data generation, model training, high-performance optimization, etc. In addition, it includes different workflows and management tools, as well as computing power scheduling tools for different systems, different scenarios, and different purposes.
It should be pointed out that the combination of physical modeling and machine learning often fundamentally changes the implementation logic of a piece of software. Therefore, the new infrastructure will not be settled once and for all, but will be gradually improved through an iterative process and upgrades from time to time.
In the long run, the DeepModeling community is committed to combining physical models at all scales with machine learning methods, using the most cutting-edge computing platforms to solve the most challenging scientific and technological problems faced by the human society.
Why Choose OpenSource?
There are different interpretations of the term "open source". The consensus among the DeepModeling community is that open source is a collaborative software development platform based on the spirit of openness and sharing. Open source is a familiar concept for people in the fields of machine learning and computer science, but it is not yet popular in the field of scientific computing. What we advocate is that an algorithm or software should not be judged by the reputation of the journal in which it is published, but by its ability to solve real world problems and its actual contribution to science. The sustainable development of a software requires continuous investment in manpower. It should undergo incremental improvement, and it should be put to the test of solving real-world problems in an open environment. This is often difficult to achieve by individuals or individual groups. The open-source community provides better solutions.
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Researchers and developers from all over the world are using DeepModeling to solve their problems.
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Researchers and developers from all over the world are using DeepModeling to solve their problems.
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Researchers and developers from all over the world are using DeepModeling to solve their problems.
We love Creators
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 deepmodeling@deepmodeling.com
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.
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.
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.
If you have questions?
We'are here to help
If you have some questions, we recommend you first go to our discussion board on github. (Our users come from different countries, so please discuss in English). Our developers will check the discussion board every day.
