Focusing on material simulation and material genomics, realizing a new paradigm for material design and intelligent screening of new materials:

1. Development of material simulation methods: Develop new theoretical simulation methods and multiscale simulation methods to achieve the optimization of complex structures of functional materials; Develop deep learning methods such as graph neural networks to accelerate the screening and design of new materials.

2. Material structure optimization: Combining density functional theory, molecular force field methods, mesoscopic scale simulation methods, etc., to study functional nanomaterials and complex molecular systems, explain experimental phenomena and mechanisms at the nanometer level, and optimize the microstructure of materials.

3. Intelligent design of materials: By building databases of functional nanomaterials, energy materials, and other materials, the relationship between material/molecular structure and performance is derived, deep learning and intelligent screening of photoelectric materials, energy materials, catalytic materials, and biological materials are conducted, and new materials are developed.

1. 材料模拟方法发展:发展新的理论模拟方法和多尺度模拟方法,实现功能材料复杂结构的优化;发展图神经网络等深度学习方法,加速新材料的筛选和设计。
2. 材料结构优化:结合密度泛函理论、分子力场方法、介观尺度模拟方法等对功能纳米材料和复杂分子体系进行研究,解释纳米级别的实验现象和机制,优化材料微观结构。
3. 材料智能设计:通过建设功能纳米材料、能源材料等数据库,给出材料/分子结构与性能之间的关系,对光电材料、能源材料、催化材料、生物材料等进行深度学习和智能筛选,发展新材料。