Lass, M., Kenter, T., Plessl, C., &; Brehm, M. (2024). Characterizing Microheterogeneity in Liquid Mixtures via Local Density Fluctuations. Entropy, 26(4), Article 322.https://doi.org/10.3390/e26040322 

Schapeler, T., Schade, R., Lass, M., Plessl, C., &; Bartley, T. (2024). Scalable quantum detector tomography by high-performance computing. In arXiv:2404.02844.

Bauer, C., Kenter, T., Lass, M., Mazur, L., Meyer, M., Nitsche, H., Riebler, H., Schade, R., Schwarz, M., Winnwa, N., Wiens, A., Wu, X., Plessl, C., &; Simon, J. (2024). Noctua 2 Supercomputer. Journal of Large-Scale Research Facilities, 9.https://doi.org/10.17815/jlsrf-8-187  

Schade, R., Kenter, T., Elgabarty, H., Lass, M., Kühne, T., &; Plessl, C. (2023). Breaking the exascale barrier for the electronic structure problem in ab-initio molecular dynamics. The International Journal of High Performance Computing Applications, Article 109434202311776.https://doi.org/10.1177/10943420231177631 

Van Hirtum, L., De Causmaecker, P., Goemaere, J., Kenter, T., Riebler, H., Lass, M., &; Plessl, C. (2023). A computation of D(9) using FPGA Supercomputing. In arXiv:2304.03039.

Lass, M. (2022). Bringing Massive Parallelism and Hardware Acceleration to Linear Scaling Density Functional Theory Through Targeted Approximations. Universität Paderborn.https://doi.org/10.17619/UNIPB/1-1281 

Schade, R., Kenter, T., Elgabarty, H., Lass, M., Schütt, O., Lazzaro, A., Pabst, H., Mohr, S., Hutter, J., Kühne, T., &; Plessl, C. (2022). Towards electronic structure-based ab-initio molecular dynamics simulations with hundreds of millions of atoms. Parallel Computing, 111, Article 102920.https://doi.org/10.1016/j.parco.2022.102920 

Kühne, T., Iannuzzi, M., Ben, M. D., Rybkin, V. V., Seewald, P., Stein, F., Laino, T., Khaliullin, R. Z., Schütt, O., Schiffmann, F., Golze, D., Wilhelm, J., Chulkov, S., Mohammad Hossein Bani-Hashemian, M. H. B.-H., Weber, V., Borstnik, U., Taillefumier, M., Jakobovits, A. S., Lazzaro, A.,https://doi.org/10.1063/5.0007045__Hutter, J. (2020). CK: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations. The Journal of Chemical Physics, 152(19), Article 194103.https://doi.org/10.1063/5.0007045 

Lass, M., Schade, R., Kühne, T., &; Plessl, C. (2020). A Submatrix-Based Method for Approximate Matrix Function Evaluation in the Quantum Chemistry Code CK. Proc. International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 1127–1140.https://doi.org/10.1109/SC41405.2020.00084 

Rengaraj, V., Lass, M., Plessl, C., &; Kühne, T. (2020). Accurate Sampling with Noisy Forces from Approximate Computing. Computation, 8(2), Article 39.https://doi.org/10.3390/computation8020039 

Richters, D., Lass, M., Walther, A., Plessl, C., &; Kühne, T. (2019). A General Algorithm to Calculate the Inverse Principal p-th Root of Symmetric Positive Definite Matrices. Communications in Computational Physics, 25(2), 564–585.https://doi.org/10.4208/cicp.OA-2018-0053 

Lass, M., Kühne, T., &; Plessl, C. (2018). Using Approximate Computing for the Calculation of Inverse Matrix p-th Roots. Embedded Systems Letters, 10(2), 33–36.https://doi.org/10.1109/LES.2017.2760923 

Lass, M., Mohr, S., Wiebeler, H., Kühne, T., &; Plessl, C. (2018). A Massively Parallel Algorithm for the Approximate Calculation of Inverse p-th Roots of Large Sparse Matrices. Proc. Platform for Advanced Scientific Computing (PASC) Conference. Platform for Advanced Scientific Computing Conference (PASC), Basel, Switzerland.https://doi.org/10.1145/3218176.3218231 

Riebler, H., Lass, M., Mittendorf, R., Löcke, T., &; Plessl, C. (2017). Efficient Branch and Bound on FPGAs Using Work Stealing and Instance-Specific Designs. ACM Transactions on Reconfigurable Technology and Systems (TRETS), 10(3), 24:1-24:23.https://doi.org/10.1145/3053687 

Lass, M., Leibenger, D., &; Sorge, C. (2016). Confidentiality and Authenticity for Distributed Version Control Systems - A Mercurial Extension. In Proc. 41st Conference on Local Computer Networks (LCN). IEEE.https://doi.org/10.1109/lcn.2016.11 

Lass, M., Kühne, T., &; Plessl, C. (2016). Using Approximate Computing in Scientific Codes. Workshop on Approximate Computing (AC).

Lass, M. (2015). Localization and Analysis of Code Paths Suitable for Acceleration using Approximate Computing. Paderborn: Paderborn University.

Lass, M. (2013). Sichere Speicherung vertraulicher Daten in verteilten Versionskontrollsystemen. Paderborn: Paderborn University.