张达威，北京科技大学新材料技术研究院教授，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，兼任美国材料性能与保护协会国际顾问委员会主席，Corrosion Science期刊副主编， MGE Advances、npj Materials Degradation等10本国际期刊的编委，以及ISO微生物腐蚀领域全球召集人。主要从事智能耐蚀材料技术方面的研究工作，包括智能防腐涂层、微生物腐蚀与防护、人工智能驱动的耐蚀材料设计等，发表SCI论文200余篇，引用13000余次（h因子60）；入选国家级青年人才称号，当选美国材料性能与保护协会会士，获材料基因工程青年科学家奖（一等奖），以第一完成人获得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，入选2023中国高被引学者。指导博士生获欧洲腐蚀联合会青年科学家奖、美国国际腐蚀工程师协会亚太青年学者奖；作为导师托举多人入选中国科协青年人才托举工程。

1. 智能防腐涂层
2. 微生物腐蚀与防护
3. 人工智能驱动的耐蚀材料设计
4. 高通量-自动化材料腐蚀实验技术
5. 材料腐蚀模拟计算

1. 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.

2. Wang, J., Ma, L., Chen, Z., Wang, Y., Ren, C., Liu, T., Ma, L., Lin, C. and Zhang, D., 2023. Multi-channel preparation and high-throughput screening of coating fillers with optimized corrosion sensing and inhibition properties for smart protective coatings.  Corrosion Science ,  222 , p.111390.

3. Liu, T., Zhang, D., Zhang, R., Wang, J., Ma, L., Keil, P., Mol, A. and Li, X., 2023. Self-healing and corrosion-sensing coatings based on pH-sensitive MOF-capped microcontainers for intelligent corrosion control.  Chemical Engineering Journal ,  454 , p.140335.

4. Yang, J., Ran, Y., Liu, S., Ren, C., Lou, Y., Ju, P., Li, G., Li, X. and Zhang, D., 2023. Synergistic D‐Amino Acids Based Antimicrobial Cocktails Formulated via High‐Throughput Screening and Machine Learning.  Advanced Science , p.2307173.

5. 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.

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. Lou, Y., Chang, W., Cui, T., Qian, H., Hao, X. and Zhang, D., 2023. Microbiologically influenced corrosion inhibition induced by S. putriefaciens mineralization under extracellular polymeric substance regulation via FlrA and FlhG genes.  Corrosion Science ,  221 , p.111350.

8. Cui, T., Qian, H., Chang, W., Zheng, H., Guo, D., Kwok, C.T., Tam, L.M. and Zhang, D., 2023. Towards understanding Shewanella algae-induced degradation of passive film of stainless steel based on electrochemical, XPS and multi-mode AFM analyses.  Corrosion Science ,  218 , p.111174.

9. 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.

10. Dai, J., Fu, D., Song, G., Ma, L., Guo, X., Mol, A., Cole, I. and Zhang, D., 2022. Cross-category prediction of corrosion inhibitor performance based on molecular graph structures via a three-level message passing neural network model.  Corrosion Science ,  209 , p.110780.

11. Hu, Y., Huang, L., Lou, Y., Chang, W., Qian, H. and Zhang, D., 2022. Microbiologically influenced corrosion of stainless steels by Bacillus subtilis via bidirectional extracellular electron transfer.  Corrosion Science ,  207 , p.110608.

12. Cui, T., Qian, H., Lou, Y., Chen, X., Sun, T., Zhang, D. and Li, X., 2022. Single-cell level investigation of microbiologically induced degradation of passive film of stainless steel via FIB-SEM/TEM and multi-mode AFM.  Corrosion Science ,  206 , p.110543.

13. Liu, T., Zhang, D., Ma, L., Huang, Y., Hao, X., Terryn, H., Mol, A. and Li, X., 2022. Smart protective coatings with self‐sensing and active corrosion protection dual functionality from pH-sensitive calcium carbonate microcontainers.  Corrosion Science ,  200 , p.110254.

14. Qian, H.C., Chang, W.W., Cui, T.Y., Li, Z., Guo, D.W., Kwok, C.T., Tam, L.M. and Zhang, D.W., 2021. Multi-mode scanning electrochemical microscopic study of microbiologically influenced corrosion mechanism of 304 stainless steel by thermoacidophilic archaea.  Corrosion Science ,  191 , p.109751.

15. 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.

16. Liu, T., Zhao, H., Zhang, D., Lou, Y., Huang, L., Ma, L., Hao, X., Dong, L., Rosei, F. and Lau, W.M., 2021. Ultrafast and high-efficient self-healing epoxy coatings with active multiple hydrogen bonds for corrosion protection.  Corrosion Science ,  187 , p.109485.

17. Ma, L., Ren, C., Wang, J., Liu, T., Yang, H., Wang, Y., Huang, Y. and Zhang, D., 2021. Self-reporting coatings for autonomous detection of coating damage and metal corrosion: A review.  Chemical Engineering Journal ,  421 , p.127854.

18. Ma, L., Wang, J., Zhang, D., Huang, Y., Huang, L., Wang, P., Qian, H., Li, X., Terryn, H.A. and Mol, J.M., 2021. Dual-action self-healing protective coatings with photothermal responsive corrosion inhibitor nanocontainers.  Chemical Engineering Journal ,  404 , p.127118.

19. Wu, D., Zhang, D., Liu, S., Jin, Z., Chowwanonthapunya, T., Gao, J. and Li, X., 2020. Prediction of polycarbonate degradation in natural atmospheric environment of China based on BP-ANN model with screened environmental factors.  Chemical Engineering Journal ,  399 , p.125878.

20. Pei, Z., Zhang, D., Zhi, Y., Yang, T., Jin, L., Fu, D., Cheng, X., Terryn, H.A., Mol, J.M. and Li, X., 2020. Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning.  Corrosion science ,  170 , p.108697.