Publications
Publications
Publications spanning computational thermodynamics, machine learning, scientific workflows, research software and AI for science, newest first. Publications marked Selected represent the breadth of the group's research.
2026
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Toward Full Interoperability in Materials Science: Integrating Workflows With Knowledge Graphs
J. Janssen et al. · Advanced Engineering Materials (2026)
doi:10.1002/adem.71258 -
Supporting AI Readiness Through Digital Workflows in Materials Science
M. Bruns et al. · Advanced Engineering Materials (2026)
doi:10.1002/adem.71262 -
pysqa – Python Simple Queuing System Adapter Selected
J. Janssen and J. Neugebauer · Journal of Open Source Software (2026)
doi:10.21105/joss.10961 -
ALF: Open-Source Active Learning Framework for Atomistic Modeling
V. F. Grizzi et al. · Journal of Chemical Theory and Computation (2026)
doi:10.1021/acs.jctc.6c01122 -
A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science
S. Brinckmann et al. · Advanced Engineering Materials (2026)
doi:10.1002/adem.202503176 -
Deep Gaussian process-based cost-aware batch Bayesian optimization for complex materials design campaigns
Sk M. A. A. Alvi et al. · npj Computational Materials 12, 105 (2026)
doi:10.1038/s41524-026-01981-7 -
Computationally accelerated experimental materials characterization – drawing inspiration from high-throughput simulation workflows
M. Stricker et al. · npj Computational Materials 12, 2 (2026)
doi:10.1038/s41524-025-01919-5 -
Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost Selected
I. Baghishov et al. · Digital Discovery 5 (2026)
doi:10.1039/d5dd00294j
2025
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Accurate and uncertainty-aware multi-task prediction of HEA properties using prior-guided deep Gaussian processes
Sk M. A. A. Alvi et al. · npj Computational Materials 11, 306 (2025)
doi:10.1038/s41524-025-01811-2 -
A Python workflow definition for computational materials design Selected
J. Janssen et al. · Digital Discovery 4, 3149–3161 (2025)
doi:10.1039/d5dd00231a -
32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery Selected
Y. Zimmermann et al. · Machine Learning: Science and Technology 6, 030701 (2025)
doi:10.1088/2632-2153/ae011a -
Executorlib – Up-scaling Python workflows for hierarchical heterogeneous high-performance computing Selected
J. Janssen et al. · Journal of Open Source Software 10, 7782 (2025)
doi:10.21105/joss.07782 -
Hierarchical Gaussian process-based Bayesian optimization for materials discovery in high entropy alloy spaces
Sk M. A. A. Alvi et al. · Acta Materialia 289 (2025)
doi:10.1016/j.actamat.2025.120908
2024
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Automated optimization and uncertainty quantification of convergence parameters in plane wave density functional theory calculations Selected
J. Janssen, E. Makarov, T. Hickel, A. V. Shapeev and J. Neugebauer · npj Computational Materials 10, 263 (2024)
doi:10.1038/s41524-024-01388-2 -
On the Importance of Configuration Search to the Predictivity of Lanthanide Selectivity
T. J. Summers et al. · JACS Au 5, 2 (2024)
doi:10.1021/jacsau.4c00770 -
From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows Selected
S. Menon et al. · npj Computational Materials 10, 261 (2024)
doi:10.1038/s41524-024-01441-0 -
Linear graphlet models for accurate and interpretable cheminformatics
M. Tynes et al. · Digital Discovery (2024)
doi:10.1039/d4dd00089g
2023
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Architector for high-throughput cross-periodic table 3D complex building
M. G. Taylor et al. · Nature Communications 14 (2023)
doi:10.1038/s41467-023-38169-2 -
FitSNAP: Atomistic machine learning with LAMMPS
A. Rohskopf et al. · Journal of Open Source Software 8, 84 (2023)
doi:10.21105/joss.05118
2021
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A fully automated approach to calculate the melting temperature of elemental crystals
L.-F. Zhu, J. Janssen, S. Ishibashi, F. Körmann, B. Grabowski and J. Neugebauer · Computational Materials Science 187 (2021)
doi:10.1016/j.commatsci.2020.110065
2019
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pyiron: An integrated development environment for computational materials science Selected
J. Janssen et al. · Computational Materials Science 163, 24–36 (2019)
doi:10.1016/j.commatsci.2018.07.043
This list was compiled from the public Jan Janssen and MPI SusMat profiles in October 2026; DOIs verified via Crossref. For the most current record, use the linked databases.