AI4Science101 新手营地：关于AI for Science，你想了解的一切都在这里

想要知道Science领域有哪些亟待AI帮助解决的挑战？

想要知道AI的知识能够如何应用到Science中？

想要系统的学习AI4Science相关知识，但是找不到合适的渠道？

这里，将是你学习AI4Science的最佳场所！AI4Science101带你扬帆起航！

AI4Science 呼唤具有

综合素质的人才

随着人工智能的飞速发展，AI技术应用到了几乎所有的领域。近年来将AI技术应用到科学领域，即AI for Science（简称AI4Science)得到了越来越多关注，也产生了AlphaFold2, DeePMD等一系列成功的实践。而未来AI for Science进一步发展势必会对学界和业界产生非常深远的影响。

AI4Science的时代对人才的建设提出了全新的要求。需要一大批既能够熟练掌握常见AI算法的原理，又熟悉对应学科知识的综合性人才。只有这样才能更好的将AI算法应用到科学领域，推动AI4Science领域的全面发展与进步。

然而传统AI人才精通各种机器学习模型，但在科学领域的基础较为薄弱；传统科学领域人才精通具体领域的科学原理，却对机器学习的算法所知甚少。而目前并没有一套成熟的体系指导大家如何进行AI4Science的学习与实践——学习者只能自己通过漫无目的的搜索和碎片化的知识进行学习。

AI4Science101项目简介

基于这个原因，从2022年3月份开始，DeepModeling社区几位志愿者开始筹备AI4Science101的项目。该项目希望通过以一系列教学文档的形式，帮助所有对AI4Science感兴趣的人能够快速了解该领域。既包括了解该领域的核心概念与重要挑战，也能够进行具体相关领域的学习。考虑到我们希望能够让全世界所有对该领域感兴趣的人都可以参与到学习中，我们以英文作为教学文档的主要语言，并且将内容放在网站和github上，方便大家审阅与查看。

如果你是AI领域的人才想要在AI4Science大显身手，或者你是Science领域的人才想要将AI应用到你的研究中，又或者你是低年级本科生想要打好自己AI4Science的基础，再或者你仅仅是想更加深入了解AI4Science领域的研究与挑战，欢迎关注这个项目！在这里你一定会有所收获！

AI4Science101项目网站

项目网站：

ai4science101.deepmodeling.com

项目github：

https://github.com/deepmodeling/AI4Science101

项目讨论版：

https://github.com/deepmodeling/AI4Science101/discussions

AI4Science第一期项目

目前AI4Science的第一期已经完成并对外公布。第一期文档分成5个部分：

1. Announcing AI for Science Blog Series：介绍AI4Science101项目的背景

1. AI for Scientific Discovery：为AI背景的同学介绍什么是AI4Science，该领域的成功案例和未来的机会。并为AI领域的人才提供了一个学习Science知识的路线图。

1. Scientific Discovery in the era of AI：为Science背景的同学介绍什么是AI4Science，介绍AI的相关概念和基础原理，如何推动Science发展，并为Science领域的人才提供了一个学习AI知识的路线图。

2. Molecular Dynamics：介绍分子动力学相关知识

3. Knowledge Base：关于科学领域的核心概念解释，主要包括物理、化学、生物、医药4个部分。

想要进行AI4Science101的

学习与交流？

除了阅读文档外，你还可以加入AI4Science101交流群，和群里的朋友们一起讨论AI4Science相关知识。

（如果群二维码失效，可以添加深度势能小编微信：deeppotential，小编会手动拉你入群）

另外我们也鼓励你在github discussion版进行讨论，尤其是当你有一些好的内容进行分享时，发到discussion版将加大方便资料保存和整理。

想要参与AI4Science101项目？

如果你对AI或者Science某一个领域有比较深入的研究，并且愿意将知识分享给大家，我们鼓励你参与到AI4Science 101的项目中。你可以邮件联系：[e-mail]进行报名，团队的志愿者会尽快和你联系。

附录：AI4Science101 Anouncement

Announcing AI for Science Blog Series

· Background

With the rapid development of AI, people have started to apply AI methods to almost every field, from natural language processing to computer vision. Recent breakthroughs have demonstrated the power of AI in solving grand challenges in the scientific community. Particular examples include predicting highly accurate protein structures with AlphaFold2, simulating 100 million particle systems with DPMD, imagining the first-ever picture of a black hole, etc. Nevertheless, many researchers in both AI and scientific fields are not able to approach AI for Science research due to many gaps, from limited domain knowledge to the misunderstanding of AI capability. In addition, the educational materials for AI for science are scattered and poorly organized. We announce this initiative (a blog series) to bring people who are interested in AI for Science into the forefront of AI for Science with knowledge collected at different levels, from motivational overview of the field, and lecture-style tutorials on specific topics to knowledge base over common terminologies.

· Aim and Scope

We are a group of students, researchers, and practitioners who are interested in AI for science and devoted to advancing AI for science as a new field and community. We write blogs to promote AI for science research at different levels from motivations for new researchers, resources for interdisciplinary researchers, etc. As we announce this AI for science blog series, we release two main documents with titles *AI for Scientific Discovery* and *Scientific Discovery in the era of AI*, which are different views on AI for science from the AI and scientific communities. In addition, we compile a list of common terminologies in different disciplines as a *knowledge base*. As our first *lecture-style tutorial*, we highlight a study of molecular dynamics, one of the most commonly used tools in computational chemistry.

· Acknowledgement

The project is a part of the DeepModeling community, an open-source community that aims to define the future of scientific computing together.

This effort is primarily led by Yuanqi Du (Cornell), Yingze Wang (UCB), Yanze Wang (PKU), Yibo Wang (DP) and contributors Jiayue Wang (DP), Jiameng Huang (PKU), Arian Jamasb (Cambridge), Jihao Long (Princeton), Guiyu Cao (PKU), Zhenfeng Deng (PKU), Xi Chen (DP), Siyuan Zhou (BFSU), Yinkai Wang (Tufts). We also like to express our gratitude to Weinan E (Princeton \& PKU), Linfeng Zhang (DP), Ping Tuo (DP), Zheng Cheng (AISI), Han Wen (DP), Dongdong Wang (DP), Xinming Tu (UW), Nilay Shah (UCLA), Hannes Stark (MIT), Chaitanya Joshi (Cambridge), Ryan-Rhys Griffiths (Cambridge), Sang Truong (Stanford), Junhan Chang (PKU), Chenbing Wang (PKU), Ziming Liu (MIT), Weiliang Luo (PKU), Zhen Wang (DP), Yucheng Zhang (UTokyo), Ferry Hooft (UvA), Ziyao Li (PKU) for providing expertise, feedback and support.

· Feedback/comment or Join us

Please reach out to us at [e-mail] if you have any feedback or comments.

As this is a community effort, we welcome anyone interested to join us. Any kind of volunteer work is welcomed, including writing tutorials, drawing illustrations, etc. Do not hesitate to let us know!

· Contribution Guidelines

We are looking for contributors/experts for specific areas related to AI for Science. The expected contributions include a three-level write-up, a one-paragraph introduction and learning material in section 2 or 3 (depending on the topic in AI or Science), common terminologies and short explanations in section 5, and a specialized chapter similar to section 4. For each specialized chapter, we expect to include (1) target audience and motivations, (2) brief review of literature/history, (3) current advances and future promises, (4) takeaways, and (5) a running sample/demo (optional).

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