Academics

Total Hours: /

Credits: /

Assessment: Assignments + Group project

I. Course Objective

Master the core concepts of AI for Materials and the research frontiers in materials science; understand the technical paradigm of the interdisciplinary field of AI + Materials.

Grasp the key principles of preparation, characterization, and performance testing for three major material systems: new energy materials, organic polymer materials, and inorganic photocatalytic materials; establish a closed-loop understanding of algorithms, hardware, and experimentation.

Become familiar with the hardware architecture, communication interfaces, and control logic of robotic arms and pump valves in automated experimental platforms; master the operation of the Uni-Lab platform and the basics of secondary development.

Develop rigorous scientific research practices and awareness of automated safety operations; cultivate open and innovative thinking, research collaboration skills, and the ability to present research outcomes; build interdisciplinary problem-solving skills for complex materials challenges.

II. References

Machine Intelligence and Scientific Experiment, Peking University, Mo Fanyang

Foundations of Machine Learning, published by China Machine Press, authors: Mehryar Mohri, Afshin Rostamizadeh, publication date: May 1, 2019

Comprehensive Experimental Course on Robotics, published by China Machine Press, editor‑in‑chief: Li Dazhai