个人简介：

张达威，教授，博士生导师，国家“万人计划”科技创新领军人才。本科毕业于同济大学，分别于美国Worcester Polytechnic Institute和Texas A&M University获得硕士与博士学位。现任北京科技大学国际合作与交流处处长，国家材料腐蚀与防护科学数据中心常务副主任，北京材料基因工程高精尖创新中心副主任，教育部“一带一路”材料腐蚀野外站主任；美国材料性能与保护协会会士、亚洲材料数据委员会主席，兼任Corrosion Science副主编以及npj Materials Degradation、MGE Advances等10余本国际期刊编委。

主要从事智能耐蚀材料技术方面的研究工作，作为负责人承担材料2030重大专项项目、国家自然科学基金企业联合重点项目、国家重点研发计划课题等项目；发表SCI论文300余篇，引用17000余次（h因子70），入选2023、2024中国高被引学者；授权发明专利40余项，包括美国专利4项；获得材料基因工程青年科学家奖（一等奖），以及中国腐蚀与防护学会自然科学一等奖和冶金科学与技术二等奖（第一完成人）。

研究方向：

1. 智能防腐涂层
2. 人工智能驱动的耐蚀材料设计

3. 高通量-自动化材料腐蚀实验技术

4. 材料腐蚀模拟计算

5. 微生物腐蚀与抗菌材料

科研业绩：

1.Wang, J., Liu, S., Zhou, Y., Yu, S., Chen, Z., Li, Z., Chen, W., Li, N., Ma, L. and Zhang, D., 2025. Lurid Bolete‐Inspired Damage Reporting Coating with Simultaneous Weathering and Corrosion Resistance: Construction Strategy and Real‐Time Degradation Monitoring. Advanced Functional Materials, 35(5), p.2414620.
2.Fu, Z., Gu, Z., Legut, D., Chen, X., Zhang, D. and Zhang, R., 2025. High‐Throughput Design of 2D Coating Materials for the Corrosion Protection of Copper. Advanced Functional Materials, p.2423901.
3.Winkler, D.A., Hughes, A.E., Özkan, C., Mol, A., Würger, T., Feiler, C., Zhang, D. and Lamaka, S., 2024. Impact of inhibition mechanisms, automation, and computational models on the discovery of organic corrosion inhibitors. Progress in Materials Science, p.101392.
4.Ma, L., Xu, D., Wu, S., Guo, X., Liu, T., Wei, M., Wang, J., Chen, Z. and Zhang, D., 2024. Polyurethane coatings with corrosion inhibition and color-fluorescence damage reporting properties based on APhen-grafted carbon dots. Corrosion Science, 232, p.112038.
5.Yang, J., Ran, Y., Liu, S., Ren, C., Lou, Y., Ju, P., Li, G., Li, X. and Zhang, D., 2024. Synergistic D‐Amino Acids Based Antimicrobial Cocktails Formulated via High‐Throughput Screening and Machine Learning. Advanced Science, 11(9), p.2307173.
6.Fu, Z., Guo, X., Zhang, X., Legut, D. and Zhang, D., 2024. Towards rational design of organic copper corrosion inhibitors: High-throughput computational evaluation of standard adsorption Gibbs energy. Corrosion Science, 227, p.111783.
7.Liu, T., Chen, Z., Yang, J., Ma, L., Mol, A. and Zhang, D., 2024. Machine learning assisted discovery of high-efficiency self-healing epoxy coating for corrosion protection. npj Materials Degradation, 8(1), p.11.
8.Ren, C., Ma, L., Luo, X., Dong, C., Gui, T., Wang, B., Li, X. and Zhang, D., 2023. High-throughput assessment of corrosion inhibitor mixtures on carbon steel via droplet microarray. Corrosion Science, 213, p.110967.
9.Ma, J., Dai, J., Guo, X., Fu, D., Ma, L., Keil, P., Mol, A. and Zhang, D., 2023. Data-driven corrosion inhibition efficiency prediction model incorporating 2D–3D molecular graphs and inhibitor concentration. Corrosion Science, 222, p.111420.
10.Li, Z., Chang, W., Cui, T., Xu, D., Zhang, D., Lou, Y., Qian, H., Song, H., Mol, A., Cao, F. and Gu, T., 2021. Adaptive bidirectional extracellular electron transfer during accelerated microbiologically influenced corrosion of stainless steel. Communications Materials, 2(1), p.67.