Authors: Jeliazkova, Nina | Kochev, Nikolay | Radeva, Lyudvika | ILIEV, Luchesar | Jeliazkov, Vedrin | CAMPANA, GIAMPAOLO
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

Automated SSbD Workflows Using FAIR Electronic Laboratory Notebooks:Application to Polymer Informatics Safe-and-Sustainable-by-Design (SSbD) is a voluntary European framework guiding chemical and material development towards sustainability, but practical implementations face gaps in data, AI validation, interoperable workflows, and collaborative tools 1. We present a digital framework2 that addresses these gaps by combining a FAIR-compliant Electronic Lab Notebook (eLabFTW3) with a graph-based representation of SSbD workflows. Materials, processes, lab results, and decisions are captured as structured, linked resources, enabling transparency and traceability across multidisciplinary teams. The ELN can be populated manually by researchers or automatically via scripts that retrieve data from public or internal databases (e.g., AMBIT, CompTox, REACH) and create structured entries. Python workflows access the ELN API to extract metadata, experimental outcomes, and decisions to generate SSbD reports, including hazard tables and selection rationales. Predictive model outputs - from VEGA4, EPA tools5, and other QSAR models - are processed through conformal prediction6, a model-agnostic framework that provides prediction sets or intervals that are guaranteed to contain the true outcome with a user-specified confidence level (e.g., 90%). Rather than relying on a single model or a fixed threshold, conformal prediction enables integration of multiple predictive models by aggregating their prediction sets, for example by intersection or union, or by weighting based on the interval width. To demonstrate the scalability of the approach, we developed a cheminformatics pipeline for PFAS-alternative polymer screening. Using SLN7 and SMIRKS8 notation, 7.7 million polymer candidates were generated and filtered based on experimental feasibility and lab-scale constraints. Simulated chemical transformations yielded 7.5 million final structures. Surface tension was used as a screening property, estimated using an empirical relation with cohesive energy density (CED). CED was computed using two QSPR models: Fedors’ method (via Ambit-GCM9) and Zhao’s method (via CDK10), with 118 SMARTS patterns enabling automated group-based property estimation. This approach links automated candidate generation, predictive evaluation, and structured documentation into a unified SSbD workflow. It supports transparent decision-making, reproducibility, and co-creation, and is adaptable across material domains. Link to tools and example implementations are available at https://sbd.adma.ai. Acknowledgements: This work has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101092164 (ZeroF) and No. 101130073 (PHAntastic). Also funded by the Swiss State Secretariat for Education, Research and Innovation (SERI). References 1. Leopold, A. et al. Recommendations and Research Gaps to Improve the Function of the Safe and Sustainable by Design (SSbD) concept: SETAC – EC Consultation series 2023-24. https://zenodo.org/records/14228553 (2024) doi:10.5281/zenodo.14228553. 2. Jeliazkova, N., Kochev, N., Iliev, L., Jeliazkov, V. & Campana, G. Towards Addressing Co-Creation Gaps: Automating Safe-and-Sustainable-by-Design Workflows with Electronic Lab Notebooks. in 21st Global Conference on Sustainable Manufacturing (GCSM 2025) accepted (2025). 3. CARPi, N., Minges, A. & Piel, M. eLabFTW: An open source laboratory notebook for research labs. J. Open Source Softw. 2, 146 (2017). 4. VEGA-QSAR. Virtual models for Evaluating the properties of chemicals within a Global Architecture. http://www.vega-qsar.eu/. 5. Lowe, C. N. & Williams, A. J. Enabling High-Throughput Searches for Multiple Chemical Data Using the U.S.-EPA CompTox Chemicals Dashboard. J. Chem. Inf. Model. 61, 565–570 (2021). 6. Angelopoulos, A. N. & Bates, S. Conformal Prediction: A Gentle Introduction. Found. Trends® Mach. Learn. 16, 494–591 (2023). 7. Kochev, N., Jeliazkova, N. & Tancheva, G. Ambit‐SLN: an Open Source Software Library for Processing of Chemical Objects via SLN Linear Notation. Mol. Inform. 40, 2100027 (2021). 8. Kochev, N., Avramova, S. & Jeliazkova, N. Ambit-SMIRKS: a software module for reaction representation, reaction search and structure transformation. J. Cheminform. 10, 42 (2018). 9. Kochev, N., Paskaleva, V., Pukalov, O. & Jeliazkova, N. Ambit-GCM: An Open-source Software Tool for Group Contribution Modelling. Mol. Inform. (2019) doi:10.1002/minf.201800138. 10. Willighagen, E. L. et al. The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. J. Cheminform. 9, 33 (2017). Workflow ; Python (programming language) ; Scalability ; Pipeline (software) ; Scripting language ; Traceability ; Interoperability Machine Learning in Materials Science ; Scientific Computing and Data Management ; Chemistry and Chemical Engineering