
Over nine months, Janssen engineered robust scientific software and packaging solutions across the pyiron_atomistics and conda-forge/staged-recipes repositories. He modernized build systems, stabilized CI/CD pipelines, and expanded data parsing and conversion utilities, focusing on Python and YAML for configuration and environment management. Janssen integrated LAMMPS and VASP parsers, improved test reliability, and enhanced dependency compatibility, enabling smoother simulation workflows and reproducible builds. He also delivered install-ready conda packaging for NOMAD parser plugins and microstructure tools, streamlining distribution and automation. His work demonstrated depth in Python development, build management, and scientific data handling, resulting in more reliable, maintainable research software.

Monthly summary for 2025-10 focusing on the packaging work for NOMAD parser plugins within the conda-forge/staged-recipes repository. No major bug fixes reported this month; primary emphasis was delivering install-ready packaging to broaden accessibility and reproducibility of NOMAD parser plugins.
Monthly summary for 2025-10 focusing on the packaging work for NOMAD parser plugins within the conda-forge/staged-recipes repository. No major bug fixes reported this month; primary emphasis was delivering install-ready packaging to broaden accessibility and reproducibility of NOMAD parser plugins.
September 2025 monthly summary for pyiron_atomistics (pyiron/pyiron_atomistics) Key features delivered: - Mendeleev property data enhancements: Updated MENDELEEV_PROPERTY_LIST with new properties and corrections; fixed VASP test dictionary key representation. Commits: f92e18de00c34591b8c68515c0f316f030f07b42. Major bugs fixed: - Robust dictionary initialization in Lammps and Vasp functions: Default dictionary arguments set to None and initialized to empty dicts to prevent uninitialized-dict errors. Commit: c844b556534fdb6524510025e9d75fe04f7bda7a. - Test suite stability enhancements: Refactored assertions to np.all, removed unused periodic table property, and fixed VASP string type comparisons. Commits: 25df2e9fd0951e1293de20d9baa6104e1c18a097; 79e9a93c133bc306c14d1b5fad784dc84d27fb7a. Dependency management and environment upgrades: - Consolidated updates across numpy, matplotlib, pandas, pyiron_base, and mendeleev; CI configurations updated for broader compatibility. Commits include: ee5ab5fdc9c568a513e0359f38b016ce72b3b91b, 752e35b59f63f018ef7b93a11af9989cf5483219, 83d1ff7f71d6b90230cb6718c50f20f88a23fcec, bb10872fa51849d9b581f751d3f1ca89d0642a8d, 4deebfe91dc9139ee3a1b0f015c438b7bcf47e47, 9c7bd46c7cb922693898adb18e93a7ee3d92a4e1, d711b0da597bec32854769e8c3b365ef1d3908ee, c18df999cb20c7a416bf3ec08b1e7bb460ae28c0, 68589d77bb023d4cc4f7a4d7471bd0204fa08b34, 188e0c9f3bc6239b22e495cb43096ed85ae27d61, 4bf060e96b8e11878ccb94d5b96bf2524f620ebe, 6f93b25d86d97879d060abe8635209030ec56fa5, e63b2f08a3f91bf8e5c0c02f93e2fd633f39612a, 03a2cec36541ed7a629e99a92a000620f95d44d0. Overall impact and accomplishments: - Increased robustness and reliability of atomistic workflows, reduced flaky tests, and better readiness for numpy 2.0 and Python 3.11; smoother CI and onboarding for contributors. Technologies/skills demonstrated: - Python, numpy, pandas, matplotlib; Lammps/VASP integration; Mendeleev data; test-driven development; advanced dependency management. Business value: - Faster, more reliable simulation setups, reduced maintenance costs, and improved confidence in results.
September 2025 monthly summary for pyiron_atomistics (pyiron/pyiron_atomistics) Key features delivered: - Mendeleev property data enhancements: Updated MENDELEEV_PROPERTY_LIST with new properties and corrections; fixed VASP test dictionary key representation. Commits: f92e18de00c34591b8c68515c0f316f030f07b42. Major bugs fixed: - Robust dictionary initialization in Lammps and Vasp functions: Default dictionary arguments set to None and initialized to empty dicts to prevent uninitialized-dict errors. Commit: c844b556534fdb6524510025e9d75fe04f7bda7a. - Test suite stability enhancements: Refactored assertions to np.all, removed unused periodic table property, and fixed VASP string type comparisons. Commits: 25df2e9fd0951e1293de20d9baa6104e1c18a097; 79e9a93c133bc306c14d1b5fad784dc84d27fb7a. Dependency management and environment upgrades: - Consolidated updates across numpy, matplotlib, pandas, pyiron_base, and mendeleev; CI configurations updated for broader compatibility. Commits include: ee5ab5fdc9c568a513e0359f38b016ce72b3b91b, 752e35b59f63f018ef7b93a11af9989cf5483219, 83d1ff7f71d6b90230cb6718c50f20f88a23fcec, bb10872fa51849d9b581f751d3f1ca89d0642a8d, 4deebfe91dc9139ee3a1b0f015c438b7bcf47e47, 9c7bd46c7cb922693898adb18e93a7ee3d92a4e1, d711b0da597bec32854769e8c3b365ef1d3908ee, c18df999cb20c7a416bf3ec08b1e7bb460ae28c0, 68589d77bb023d4cc4f7a4d7471bd0204fa08b34, 188e0c9f3bc6239b22e495cb43096ed85ae27d61, 4bf060e96b8e11878ccb94d5b96bf2524f620ebe, 6f93b25d86d97879d060abe8635209030ec56fa5, e63b2f08a3f91bf8e5c0c02f93e2fd633f39612a, 03a2cec36541ed7a629e99a92a000620f95d44d0. Overall impact and accomplishments: - Increased robustness and reliability of atomistic workflows, reduced flaky tests, and better readiness for numpy 2.0 and Python 3.11; smoother CI and onboarding for contributors. Technologies/skills demonstrated: - Python, numpy, pandas, matplotlib; Lammps/VASP integration; Mendeleev data; test-driven development; advanced dependency management. Business value: - Faster, more reliable simulation setups, reduced maintenance costs, and improved confidence in results.
August 2025 – pyiron_atomistics (Repository: pyiron/pyiron_atomistics). Key features delivered include modernizing the Python packaging and build process, updating configuration and CI/CD workflows to align with Hatchling, and stabilizing environment and test tooling. Major bugs fixed include a dependency tooling parsing issue due to a missing newline in environment.yml and a misconfigured unit test command impacting code coverage reporting. Overall impact: streamlined packaging and release processes, improved build reproducibility, and more reliable test metrics, enabling faster iterations and cleaner distribution artifacts. Technologies/skills demonstrated: Python packaging modernization (Versioneer to Hatchling), build/configuration/CI/CD workflow updates, dependency tooling and environment handling, test coverage tooling, and configuration management.
August 2025 – pyiron_atomistics (Repository: pyiron/pyiron_atomistics). Key features delivered include modernizing the Python packaging and build process, updating configuration and CI/CD workflows to align with Hatchling, and stabilizing environment and test tooling. Major bugs fixed include a dependency tooling parsing issue due to a missing newline in environment.yml and a misconfigured unit test command impacting code coverage reporting. Overall impact: streamlined packaging and release processes, improved build reproducibility, and more reliable test metrics, enabling faster iterations and cleaner distribution artifacts. Technologies/skills demonstrated: Python packaging modernization (Versioneer to Hatchling), build/configuration/CI/CD workflow updates, dependency tooling and environment handling, test coverage tooling, and configuration management.
Concise monthly summary for 2025-07: Delivered foundational packaging and ecosystem enablement for Microstructpy within conda-forge/staged-recipes, establishing reproducible build/test environments and enabling downstream packaging and distribution for microstructure tooling.
Concise monthly summary for 2025-07: Delivered foundational packaging and ecosystem enablement for Microstructpy within conda-forge/staged-recipes, establishing reproducible build/test environments and enabling downstream packaging and distribution for microstructure tooling.
June 2025 monthly summary focusing on key accomplishments across two repositories: pyiron/pyiron_atomistics and conda-forge/staged-recipes. Central outcomes: (1) pyiron_atomistics dependency and compatibility fix for pyiron_base 0.13.0 with environment config updates and import refactor; (2) ontolutils packaging introduced to staged-recipes with comprehensive metadata; (3) these changes improve stability, upgrade readiness, and enable downstream automation.
June 2025 monthly summary focusing on key accomplishments across two repositories: pyiron/pyiron_atomistics and conda-forge/staged-recipes. Central outcomes: (1) pyiron_atomistics dependency and compatibility fix for pyiron_base 0.13.0 with environment config updates and import refactor; (2) ontolutils packaging introduced to staged-recipes with comprehensive metadata; (3) these changes improve stability, upgrade readiness, and enable downstream automation.
March 2025: Delivered a new Developer Tool – External Executable Runner in the pyiron/executorlib repository, enabling internal subprocess-based execution of external executables with inter-process communication (send input, read output) and a graceful shutdown workflow. Implemented fixes to the tool’s menu to improve stability and usability. This work establishes a reliable primitive for testing and automating interactions with external scripts, accelerating developer workflows and integration testing.
March 2025: Delivered a new Developer Tool – External Executable Runner in the pyiron/executorlib repository, enabling internal subprocess-based execution of external executables with inter-process communication (send input, read output) and a graceful shutdown workflow. Implemented fixes to the tool’s menu to improve stability and usability. This work establishes a reliable primitive for testing and automating interactions with external scripts, accelerating developer workflows and integration testing.
February 2025 – pyiron_atomistics: Delivered a focused set of feature enhancements and stability fixes that improve parsing, data extraction, and documentation, enabling more reliable end-to-end simulations and faster workflows. Key features delivered include a CI environment update to stabilize builds, integration of LAMMPS and VASP parsers for broader data support, POSCAR content access via get_poscar_content, and expanded data conversion utilities, plus a foundational electronic structure module with targeted bug fixes.
February 2025 – pyiron_atomistics: Delivered a focused set of feature enhancements and stability fixes that improve parsing, data extraction, and documentation, enabling more reliable end-to-end simulations and faster workflows. Key features delivered include a CI environment update to stabilize builds, integration of LAMMPS and VASP parsers for broader data support, POSCAR content access via get_poscar_content, and expanded data conversion utilities, plus a foundational electronic structure module with targeted bug fixes.
January 2025: Focused on stabilizing the pyiron_atomistics test suite and aligning dependencies to support reliable builds and downstream product quality. Delivered a robust set of test improvements for Sphinx parsing and SCF outputs, and updated dependency bounds to remain compatible with newer atomistics, SciPy, and structuretoolkit versions. These changes reduce flaky tests, improve accuracy, and enable faster feature delivery with greater confidence.
January 2025: Focused on stabilizing the pyiron_atomistics test suite and aligning dependencies to support reliable builds and downstream product quality. Delivered a robust set of test improvements for Sphinx parsing and SCF outputs, and updated dependency bounds to remain compatible with newer atomistics, SciPy, and structuretoolkit versions. These changes reduce flaky tests, improve accuracy, and enable faster feature delivery with greater confidence.
For 2024-11, focused on expanding material property data coverage and maintaining compatibility with evolving dependencies in pyiron_atomistics. The work enhances data completeness for Mendeleev properties and ensures reliable retrieval by aligning with newer mendeleev releases, supporting downstream analyses and simulations.
For 2024-11, focused on expanding material property data coverage and maintaining compatibility with evolving dependencies in pyiron_atomistics. The work enhances data completeness for Mendeleev properties and ensures reliable retrieval by aligning with newer mendeleev releases, supporting downstream analyses and simulations.
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