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.

Semester	Course	Task
SS 2021	Point processes	Lecturer (including exercise classes)
SS 2016	Stochastik I	Teaching assistant
WS 2015/16	Elementare Wahrscheinlichkeitsrechnung und Statistik	Teaching assistant
SS 2015	Räumliche Statistik	Teaching assistant
WS 2014/15	Stochastics II	Teaching assistant
SS 2014	Methods of Monte Carlo Simulation II	Teaching assistant