Zhangxin

Zhang Xin, female, professor, Ph.D.supervisor, and Deputy Dean of the College of Chemistry. Received a Ph.D. in Science from Xiamen University in 2009, and conducted postdoctoral research at Beijing Normal University from 2009 to 2011.Since 2011, I have been teaching at the School of Chemistry, Beijing University of Chemical Technology. I have been engaged in teaching physical chemistry for undergraduate students for many years, completing 112 teaching hours per year, and achieving excellent results in comprehensive teaching quality certification.Selected for the 'Yangtze River Scholar Incentive Program' for Young Scholars by the Ministry of Education in 2023, and awarded the 'Young Teaching Master Award' by the Beijing Municipal Higher Education Institutions in 2021.Research focus on theoretical and computational catalysis chemistry and data-driven catalyst design. I have published over 100 SCI-indexed papers in renowned academic journals such as Nat. Commun., PNAS, etc. I have led three projects funded by the National Natural Science Foundation of China, and participated as a key member in three projects under the National Key R&D Program and key projects of Sinopec Group.I have served as a thesis review expert for the Ministry of Education's Degree Center and a science popularization expert for the China Association for Science and Technology.

(1) Simulation and design of functional materials

Multi-scale combination methods such as quantum chemistry, molecular dynamics and QM/MM were used to study the topological structure, size effect, surface adsorption, reaction mechanism and ion migration of functional materials, explore the key factors that determine the activity and selectivity of materials, and design catalytic materials with potential industrial catalytic significance.

(2) Machine learning methods predict material properties

Active learning, deep learning, big data analysis and other methods are used to study the stability, energy band structure and surface reaction performance of materials, explore the machine learning model architecture and training methods suitable for chemical reactions, establish the structure-performance relationship of materials, and realize the accelerated development and high-throughput screening of new materials.

For more than a decade, I have steadfastly upheld a teaching philosophy centered on “based on undergraduate education, four returns”. During this time, I have served as the primary instructor for national-level outstanding undergraduate courses in “Physical Chemistry” and “Computational Chemistry”. These courses are designed for sophomore and junior undergraduate students specializing in Chemistry Excellence Program, Chemistry, Applied Chemistry, and Energy Chemistry. Over the past three years, I have consistently dedicated an annual average of 112 teaching hours, collectively benefiting over 800 undergraduate students. The comprehensive assessment of teaching quality consistently rates my performance as “excellent”. Many of our graduates have gone on to achieve outstanding success in universities, research institutions, and state-owned enterprises. They have emerged as key contributors, actively fulfilling their roles and responsibilities in advancing science and technology for the betterment of our nation”.