Tutorial_for_AI_Next_Gen_Lab

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Tutorial_for_AI_Next_Gen_Lab

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# Tutorial_for_AI_Next_Gen_Lab
This is a project initiated by Li Jiali and yin jun. The projects contributors are from NUS, MIT and Tsinghua. We collaborate to contribute knowledge to the community.
This project is aiming to help engineering background students or researchers from different domains to gain a comprehensive and deep understanding about the methods for AI for sicence, especially the studies aiming to build the next generation lab and manufacturing.
This project will be divided into 4 major parts for AI next generation lab:
# AI for Micro-scale 
For Micro-scale, this project will focus on molecules and crystals. For this part we will follow the structure as in the paper: Crystals vs Molecules: Representation, Prediction and Inverse Design. 
## Representation
This part will focus on what information is contained (why them) and how they are representated in form of computational language. In addition,
what are the normal form of them into different ML models.
## Prediction
This part will focus on commonly used and interesting forward prediction models for molecules and crystals. (Welcome anyone can to come up with new listed models that you think are important). Will explain in detail about how the structure is designed to have different treatments of the information inputted before
## Inverse Design
This part will focus on commonly used and interesting generative models for molecules and crystals.(Welcome anyone can to come up with new listed models that you think are important). Will explain in detail about how the structure is designed to have different treatments of the information inputted before.
# AI for Device-scale 
# AI for Instrument-scale
# AI for Lab-scale

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