丁云桥

诚爱四到贤生，心到、眼到、手到、口到

匠心铸凿君璧，精学，义明，心远、志达

Sincere love and the fourfold attentions—heart, eyes, hands, and mouth—nurture a worthy student.

With artisan spirit, carve a noble jade; pursue refined learning, clear righteousness, lofty vision, and fulfilled ambition.

欢迎报考齐鲁工业大学（齐工大）理论计算化学团队本人（15066105075，微信同号）的研究生！

 本人主要研究方向聚焦于微观粒子反应机理、材料结构设计与性能预测，具体包括：

·         硅氢加成等化学反应的微观机理研究；

·         特定反应催化剂的设计与性能预测；

·         有机硅高分子的热解机制研究；

·         功能高分子的定向设计与性能预测。

     研究手段：基于数学方法和原理，结合物理模型，依托高性能服务器，采用密度泛函理论（DFT）、分子动力学（MD）等现代计算方法，力求在原子与电子尺度上深入洞悉化学过程，对真实体系进行建模与计算，并实现材料与分子的理性设计。量子计算算力的高速发展充分发挥了理论计算化学的前瞻性和精准预测功能。

AI和理论计算化学都可以研究“给定原子核与电子结构，体系会如何演化、性质如何”。理论计算会产生大量结构—性质数据， AI 更擅长从数据中学习复杂映射关系。AI 是理论计算化学的“加速器”，利用AI 的聚类、降维、符号回归等方法，从高维数据中发现隐藏的构效关系，提出新的物理图像或经验规律，反过来指导理论建模。

      如果你对理论计算化学感兴趣，渴望在微观世界中探索化学本质，欢迎加入理论与计算化学团队，一起用现代量子计算驱动化学创新。

Welcome to apply for graduate studies with me in the Theoretical and Computational Chemistry team at Qilu University of Technology (QLUT)!

My main research focuses on the reaction mechanisms of microscopic particles, material structure design, and performance prediction, specifically including:

·         Microscopic mechanism studies of chemical reactions such as hydrosilylation;

·         Design and performance prediction of catalysts for specific reactions;

·         Pyrolysis mechanisms of organosilicon polymers;

·         Targeted design and performance prediction of functional polymers.

Research methods: Based on mathematical methods and principles, combined with physical models, and relying on high-performance servers, we adopt modern computational methods such as density functional theory (DFT) and molecular dynamics (MD), striving to gain deep insight into chemical processes at the atomic and electronic scales, model and compute real systems, and achieve rational design of materials and molecules. The rapid development of quantum computing power fully leverages the foresight and precise predictive capabilities of theoretical computational chemistry.

Both AI and theoretical computational chemistry can study given the atomic nuclei and electronic structure, how will a system evolve and what properties will it have. Theoretical computations generate large amounts of structure–property data, and AI excels at learning complex mapping relationships from data. AI serves as an accelerator for theoretical computational chemistry. By using AI methods such as clustering, dimensionality reduction, and symbolic regression, we can discover hidden structure–property relationships from high-dimensional data, propose new physical pictures or empirical rules, and in turn guide theoretical modeling.

If you are interested in theoretical computational chemistry and eager to explore the essence of chemistry in the microscopic world, welcome to join the Theoretical and Computational Chemistry team, and together let us drive chemical innovation with modern quantum computing!