Ass. Prof. Dr. Matthias Neumann

• G. Gaiselmann, M. Neumann, L. Holzer, T. Hocker, M. Prestat and V. Schmidt, Stochastic 3D modeling of LSC cathodes based on structural segmentation of FIB-SEM images. Computational Materials Science 67 (2013), 48–62.
• G. Gaiselmann, M. Neumann, O. Pecho, T. Hocker, V. Schmidt and L. Holzer, Quantitative relationships between microstructure and effective transport properties based on virtual materials testing. AIChE Journal 60 (2014), 1983–1999.
• O. M. Pecho, O. Stenzel, B. Iwanschitz, P. Gasser, M. Neumann, V. Schmidt, M. Prestat, T. Hocker, R.J. Flatt and L. Holzer, 3D microstructure of Ni-YSZ anodes: Prediction of effective transport properties and optimization of redox-stability. Materials 8 (2015), 5554–5585.
• M. Neumann, J. Staněk, O. Pecho, L. Holzer, V. Beneš and V. Schmidt, Stochastic 3D modeling of complex three-phase microstructures in SOFC-electrodes with completely connected phases. Computational Materials Science 118 (2016), 353-364.
• O. Stenzel, O.M. Pecho, L. Holzer, M. Neumann and V. Schmidt, Predicting effective conductivities based on geometric microstructure characteristics. AIChE Journal 62 (2016), 1834–1843.
• M. Neumann and V. Schmidt, Stochastic 3D modeling of amorphous microstructures: A powerful tool for virtual materials testing. Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (2016).
• T. Q. Luong, N. Erwin, M. Neumann, A. Schmidt, C. Loos, V. Schmidt, M. Fändrich and R. Winter, Hydrostatic pressure increases the catalytic activity of amyloid fibril enzymes. Angewandte Chemie International Edition 55 (2016), 12412–12416.
• O. Stenzel, M. Neumann, O. Pecho, L. Holzer and V. Schmidt, Big data for microstructure-property relationships: A case study of predicting effective conductivities. AIChE Journal 63 (2017), 4224–4232.
• S. H. Ibrahim, M. Neumann, F. Klingner, V. Schmidt and T. Wejrzanowski, Analysis of the 3D microstructure of tape-cast open-porous materials via a combination of experiments and modeling. Materials & Design 133 (2017), 216-233.
• M. Kulosa, M. Neumann, M. Boeff, G. Gaiselmann, V. Schmidt and A. Hartmaier, A study on microstructural parameters for the characterization of granular porous ceramics using a combination of stochastic and mechanical modeling. International Journal of Applied Mechanics 9 (2017), 1750069.
• W. Close, M. Neumann, A. Schmidt, M. Hora, K. Annamalai, M. Schmidt, B. Reif, V. Schmidt, N. Grigorieff and M. Fändrich, Physical basis of amyloid fibril polymorphism. Nature Communications 9 (2018), 699.
• M. Neumann, O. Furat, D. Hlushkou, U. Tallarek, L. Holzer and V. Schmidt, On microstructure-property relationships derived by virtual materials testing with an emphasis on effective conductivity. In: M. Baum, G. Brenner, J. Grabowski, T. Hanschke, S. Hartmann, and A. Schöbel (eds.), Simulation Science: Proceedings of the Clausthal-Göttingen International Workshop on Simulation Science, Göttingen 2017. Communications in Computer and Information Science (CIS), Springer 2018, pp. 145-158.
• E. Machado Charry, M. Neumann, J. Lahti, R. Schennach, V. Schmidt and K. Zojer, Pore space extraction and characterization of sack paper using micro-CT. Journal of Microscopy 272 (2018), 35-46.
• M. Neumann, M. Osenberg, A. Hilger, D. Franzen, T. Turek, I. Manke, V. Schmidt, On a pluri-Gaussian model for three-phase microstructures, with applications to 3D image data of gas-diffusion electrodes. Computational Materials Science 156 (2019), 325-331.
• M. Neumann, B. Abdallah, L. Holzer, F. Willot and V. Schmidt, Stochastic 3D modeling of three-phase microstructures for predicting transport properties: a case study. Transport in Porous Media 128 (2019), 179-200.
• M. Neumann, R. Cabiscol, M. Osenberg, H. Markötter, I. Manke, J.-H. Finke and V. Schmidt, Characterization of the 3D microstructure of Ibuprofen tablets by means of synchrotron tomography. Journal of Microscopy 274 (2019), 102-113.
• O. Furat, M.Y. Wang, M. Neumann, L. Petrich, M. Weber, C.E. Krill III and V. Schmidt, Machine learning techniques for the segmentation of tomographic image data of functional materials. Frontiers in Materials 6 (2019), 145.
• M. Neumann, A.C. Wagner, N. Bohn, M. Osenberg, A. Hilger, I. Manke, J.R. Binder and V. Schmidt, Characterization of hierarchically structured electrodes with different thicknesses by means of experiments and image analysis. Materials Characterization 155 (2019), 109778.
• M. Neumann, C. Hirsch, J. Staněk, V. Beneš and V. Schmidt, Estimation of geodesic tortuosity and constrictivity in stationary random closed sets. Scandinavian Journal of Statistics 46 (2019), 848-884.
• M. Neumann, O. Stenzel, F. Willot, L. Holzer and V. Schmidt, Quantifying the influence of microstructure on effective conductivity and permeability: virtual materials testing. International Journal of Solids and Structures 184 (2020), 211-220.
• M. Weber, A. Bäuerle, M. Schmidt, M. Neumann, M. Fändrich, T. Ropinski and V. Schmidt, Automatic identification of crossovers in cryo-EM images of murine amyloid protein A fibrils with machine learning. Journal of Microscopy 277 (2020), 12-22.
• L. Petrich, G. Lohrmann, M. Neumann, F. Martin, A. Frey, A. Stoll and V. Schmidt, Detection of Colchicum autumnale in drone images, using a machine-learning approach. Precision Agriculture 21 (2020), 1291-1303.
• A.C. Wagner, N. Bohn, H. Geßwein, M. Neumann, M. Osenberg, A. Hilger, I. Manke, V. Schmidt and J.R. Binder, Hierarchical structuring of NMC111-cathode materials in lithium-ion batteries: An in-depth study of the influence of primary and secondary particle sizes on electrochemical performance. ACS Applied Energy Materials 3 (2020), 12565-12574.
• J. Le Houx, M. Osenberg, M. Neumann, J.R. Binder, V. Schmidt, I. Manke, T. Carraro and D. Kramer, Effect of tomography resolution on calculation of microstructural properties for lithium ion porous electrodes. ECS Transactions 97 (2020), 255.
• O. Birkholz, M. Neumann, M. Kamlah and V. Schmidt, Statistical investigation of structural and transport properties of densely-packed assemblies of overlapping spheres using the resistor network method. Powder Technology 378A (2021), 659-666.
• B. Prifling, M. Neumann, D. Hlushkou, C. Kübel, U. Tallarek and V. Schmidt, Generating digital twins of mesoporous silica by graph-based stochastic microstructure modeling. Computational Materials Science 187 (2021), 109934.
• M. Neumann, E. Machado Charry, K. Zojer and V. Schmidt, On variability and interdependence of local porosity and local tortuosity in porous materials: a case study for sack paper. Methodology and Computing in Applied Probability 23 (2021), 613–627.
• L. Radamaker, S. Karimi-Farsijani, G. Andreotti, J. Baur, M. Neumann, K.-H. Gührs, S. Schreiner, N. Berghaus, R. Motika, C. Haupt, P. Walther, V. Schmidt, S. Huhn, U. Hegenbart, S. Schönland, S. Wiese, C. Read, M. Schmidt and M. Fändrich, Role of mutations and post-translational modifications in systemic AL amyloidosis studied by cryo-EM. Nature Communications 12 (2021), 6434.
• M. Neumann, E. Machado Charry, E. Baikova, A. Hilger, U. Hirn, R. Schennach, I. Manke, V. Schmidt and K. Zojer, Capturing centimeter-scale local variations in paper pore space via µ-CT: A benchmark study using calendered paper. Microscopy and Microanalysis 27 (2021), 1305-1315.
• B. Prifling, M. Röding, P. Townsend, M. Neumann and V. Schmidt, Large-scale statistical learning for mass transport prediction in porous materials using 90,000 artificially generated microstructures. Frontiers in Materials 8 (2021), 786502.
• M. Neumann, M. Ademmer, M. Osenberg, A. Hilger, F. Wilde, S. Münch, M.D. Hager, U.S. Schubert, I. Manke and V. Schmidt, 3D microstructure characterization of polymer battery electrodes by statistical image analysis based on synchrotron X-ray tomography. Journal of Power Sources 542 (2022), 231783.
• B. Prifling, M. Neumann, S. Hein, T. Danner, E. Heider, A. Hoffmann, P. Rieder, A. Hilger, M. Osenberg, I. Manke, M. Wohlfahrt-Mehrens, A. Latz and V. Schmidt, Quantitative comparison of different approaches for reconstructing the carbon-binder domain from tomographic image data of cathodes in lithium-ion batteries and its influence on electrochemical properties. Energy Technology 10 (2022), 2200784.
• T. Knorr, S. Hein, B. Prifling, M. Neumann, T. Danner, V. Schmidt and A.Latz, Simulation-based and data-driven techniques for quantifying the influence of the carbon binder domain on electrochemical properties of Li-ion batteries. Energies 15 (2022), 7821.
• C. Hirsch, M. Neumann and V. Schmidt, Asymptotic properties of one-layer artificial neural networks with sparse connectivity. Statistics and Probability Letters 193 (2023), 109698.
• M. Weber, M. Neumann, M. Schmidt, P.B. Pfeiffer, A. Bansal, M. Fändrich and V. Schmidt, Segmentation and morphological analysis of amyloid fibrils from cryo-EM image data. Journal of Mathematics in Industry 13 (2023), 2.
• M. Osenberg, A. Hilger, M. Neumann, A. Wagner, N. Bohn, J.R. Binder, V. Schmidt, J. Banhart and I. Manke, Classification of FIB/SEM tomography images for highly porous multiphase materials using random forest classifiers. Journal of Power Sources 570 (2023), 233030.
• L. Holzer, P. Marmet, M. Fingerle, A. Wiegmann, M. Neumann and V. Schmidt, Tortuosity and microstructure effects in porous media: classical theories, empirical data and modern methods. Springer Series in Materials Science, volume 333 (2023).
• P. Leitl, E. Machado Charry, E. Baikova, M. Neumann, U. Hirn, V. Schmidt and K. Zojer, Joint distributions of local pore space properties quantitatively explain simulated air flow variations in paper. Transport in Porous Media 148 (2023), 627–648.
• M. Neumann, S.E. Wetterauer, M. Osenberg, A. Hilger, P. Gräfensteiner, A. Wagner, N. Bohn, J.R. Binder, I. Manke, T. Carraro and V. Schmidt. A data-driven modeling approach to quantify morphology effects on transport properties in nanostructured NMC particles. International Journal of Solids and Structures 280 (2023), 112394.
• J. Naumann, N. Bohn, O. Birkholz, M. Neumann, M. Müller, J. R. Binder, M. Kamlah. Morphology-dependent influences on the performance of battery cells with a hierarchically structured positive electrode. Batteries & Supercaps 6 (2023), e202300264.
• M. Ademmer, P.-H. Su, L. Dodell, J. Asenbauer, M. Osenberg, A. Hilger, J.-K- Chang, I. Manke, M. Neumann, V. Schmidt and D. Bresser. Unveiling the impact of crosslinking redox-active polymers on their electrochemical behavior by 3D imaging and statistical microstructure analysis. Journal of Physical Chemistry 127 (2023), 19366–19377.
• O. Furat, D.P. Finegan, Z. Yang, M. Neumann, S. Kim, T.R. Tanim, P. Weddle, K. Smith and V. Schmidt. Quantifying the impact of operating temperature on cracking in battery electrodes, using super-resolution of microscopy images and stereology. Energy Storage Materials 64 (2024), 103036.
• M. Clausnitzer, T. Danner, B. Prifling, M. Neumann, V. Schmidt and A. Latz. Influence of electrode structuring techniques on the performance of all-solid-state batteries. Batteries & Supercaps 7 (2024), e202300522.
• M. Neumann, T. Philipp, M. Häringer, G. Neusser, J. R. Binder and C. Kranz. Stochastic 3D modeling of nanostructured NVP/C active material particles for sodium-ion batteries. Batteries & Supercaps (2024), e202300409.
• P. Rieder, M. Neumann, L. Monteiro Fernandes, A. Mulard, H. Proudhon, F. Willot and V. Schmidt. Stochastic 3D microstructure modeling of twinned polycrystals for investigating the mechanical behavior of γ-TiAl intermetallics. Computational Materials Science, in print.
• M. Neumann, P. Gräfensteiner, C. Santos de Oliveira, J. Martins-Schalinski. S. Koppka, D. Enke, P. Huber and V. Schmidt. The morphology of nanoporous glass: stochastic 3D modeling, stereology and the influence of pore width. Physical Review Materials, in print.

Semester	Veranstaltung	Rolle
SS 2023	Seminar Multivariate Stochastic Modeling	Dozent
SS 2021	Point processes	Dozent und Übungsleiter
SS 2016	Stochastik I	Übungsleiter
WS 2015/16	Elementare Wahrscheinlichkeitsrechnung und Statistik	Übungsleiter
SS 2015	Räumliche Statistik	Übungsleiter
WS 2014/15	Stochastics II	Übungsleiter
SS 2014	Methods of Monte Carlo Simulation II	Übungsleiter