Submatrix and GPU-accelerated implementation of density matrix tight-binding
A. Katbashev, R. Schade, M. Laß, M. Müller, S. Grimme, A. Hansen, T. Kühne, The Journal of Chemical Physics 163 (2025).

Characterizing Microheterogeneity in Liquid Mixtures via Local Density Fluctuations
M. Lass, T. Kenter, C. Plessl, M. Brehm, Entropy 26 (2024).

Noctua 2 Supercomputer
C. Bauer, T. Kenter, M. Lass, L. Mazur, M. Meyer, H. Nitsche, H. Riebler, R. Schade, M. Schwarz, N. Winnwa, A. Wiens, X. Wu, C. Plessl, J. Simon, Journal of Large-Scale Research Facilities 9 (2024).

A Computation of the Ninth Dedekind Number Using FPGA Supercomputing
L. Van Hirtum, P. De Causmaecker, J. Goemaere, T. Kenter, H. Riebler, M. Lass, C. Plessl, ACM Transactions on Reconfigurable Technology and Systems 17 (2024) 1–28.

Scalable quantum detector tomography by high-performance computing
T. Schapeler, R. Schade, M. Lass, C. Plessl, T. Bartley, Quantum Science and Technology 10 (2024).

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

Breaking the exascale barrier for the electronic structure problem in ab-initio molecular dynamics
R. Schade, T. Kenter, H. Elgabarty, M. Lass, T. Kühne, C. Plessl, The International Journal of High Performance Computing Applications (2023).

Bringing Massive Parallelism and Hardware Acceleration to Linear Scaling Density Functional Theory Through Targeted Approximations
M. Lass, Bringing Massive Parallelism and Hardware Acceleration to Linear Scaling Density Functional Theory Through Targeted Approximations, Universität Paderborn, Paderborn, 2022.

Towards electronic structure-based ab-initio molecular dynamics simulations with hundreds of millions of atoms
R. Schade, T. Kenter, H. Elgabarty, M. Lass, O. Schütt, A. Lazzaro, H. Pabst, S. Mohr, J. Hutter, T. Kühne, C. Plessl, Parallel Computing 111 (2022).

CK: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations
T. Kühne, M. Iannuzzi, M.D. Ben, V.V. Rybkin, P. Seewald, F. Stein, T. Laino, R.Z. Khaliullin, O. Schütt, F. Schiffmann, D. Golze, J. Wilhelm, S. Chulkov, M.H.B.-H. Mohammad Hossein Bani-Hashemian, V. Weber, U. Borstnik, M. Taillefumier, A.S. Jakobovits, A. Lazzaro, H. Pabst, T. Müller, R. Schade, M. Guidon, S. Andermatt, N. Holmberg, G.K. Schenter, A. Hehn, A. Bussy, F. Belleflamme, G. Tabacchi, A. Glöß, M. Lass, I. Bethune, C.J. Mundy, C. Plessl, M. Watkins, J. VandeVondele, M. Krack, J. Hutter, The Journal of Chemical Physics 152 (2020).

A Submatrix-Based Method for Approximate Matrix Function Evaluation in the Quantum Chemistry Code CK
M. Lass, R. Schade, T. Kühne, C. Plessl, in: Proc. International Conference for High Performance Computing, Networking, Storage and Analysis (SC), IEEE Computer Society, Los Alamitos, CA, USA, 2020, pp. 1127–1140.

Accurate Sampling with Noisy Forces from Approximate Computing
V. Rengaraj, M. Lass, C. Plessl, T. Kühne, Computation 8 (2020).

A General Algorithm to Calculate the Inverse Principal p-th Root of Symmetric Positive Definite Matrices
D. Richters, M. Lass, A. Walther, C. Plessl, T. Kühne, Communications in Computational Physics 25 (2019) 564–585.

Using Approximate Computing for the Calculation of Inverse Matrix p-th Roots
M. Lass, T. Kühne, C. Plessl, Embedded Systems Letters 10 (2018) 33–36.

A Massively Parallel Algorithm for the Approximate Calculation of Inverse p-th Roots of Large Sparse Matrices
M. Lass, S. Mohr, H. Wiebeler, T. Kühne, C. Plessl, in: Proc. Platform for Advanced Scientific Computing (PASC) Conference, ACM, New York, NY, USA, 2018.

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

Confidentiality and Authenticity for Distributed Version Control Systems - A Mercurial Extension
M. Lass, D. Leibenger, C. Sorge, in: Proc. 41st Conference on Local Computer Networks (LCN), IEEE, 2016.

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

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

Sichere Speicherung vertraulicher Daten in verteilten Versionskontrollsystemen
M. Lass, Sichere Speicherung Vertraulicher Daten in Verteilten Versionskontrollsystemen, Paderborn University, Paderborn, 2013.