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【国重报告】Merging Multiscale Theory and Data Sciences to Tackle Operando Energy Conversion Systems

发布日期:2021-01-26     浏览次数:次   

报告题目:Merging Multiscale Theory and Data Sciences  to Tackle Operando Energy Conversion Systems

报告人:Karsten Reuter教授, Fritz-Haber-Institut der Max-Planck-Gesellschaft

时间:2021年01月28日16:00

地点:曾呈奎楼3楼B311

报告摘要:

Emerging operando spectroscopies and microscopies reveal a highly dynamic behavior of interfaces in energy conversion systems. Insufficient insight and the concomitant inability to control or exploit the corresponding strong structural and compositional modifications centrally limits the development of performance catalysts, electrolyzers or batteries required for a sustainable energy supply for our society. Predictive-quality modeling and simulation has become a major contributor to accelerated design all across the materials sciences, not least through powerful computational screening approaches. Current first-principles based methodology is nevertheless essentially unable to address the substantial, complex and continuous morphological transitions at working interfaces. I will review this context from the perspective of first-principles based multiscale modeling [1], highlighting that the fusion with modern machine learning approaches is likely key to tackle the true complexity of working systems. Approaches pursued by our group thereby aim at maximum data efficiency by exploiting physical models wherever possible or through active learning that only queries data on demand. Illustrative examples will be drawn from thermal methanation catalysis [2], electrocatalytic oxygen evolution [3] and organic semiconductor photovoltaics [4].

References

[1]A. Bruix, J.T. Margraf, M. Andersen, and K. Reuter, Nature Catal. 2, 659 (2019).

[2]M. Deimel, K. Reuter, and M. Andersen, ACS Catal. 10, 13729 (2020).

[3]J. Timmermann, F. Kraushofer, N. Resch, Z. Mao, M. Riva, Y. Lee, C. Staacke, M. Schmid,

C. Scheurer, G. Parkinson, U. Diebold, and K. Reuter, Phys. Rev. Lett. 125, 206101 (2020).

[4]C. Kunkel, C. Schober, J.T. Margraf, K. Reuter, and H. Oberhofer, Chem. Mater. 31, 969 (2019).

报告人简介:

Karsten Reuter教授于2020年至今任Fritz-Haber-Institut der Max-Planck-Gesellschaft理论部主任,主要研究方向是定性预测多尺度材料模、数据分析和机器学习、界面能量转换、表面纳米技术等。Karsten Reuter教授于1995年获埃尔朗根-纽伦堡大学学士学位,1998年获埃尔朗根-纽伦堡大学和马德里自治大学博士学位,2005年在柏林自由大学特许任教,2009-2020年任慕尼黑工业大学理论化学与催化研究中心主任,化学系全职教授,物理系兼职教授,已发表论文200余篇。


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