Publishing FAIR and machine-actionable reviews in materials science: The case for symbolic knowledge in neuro-symbolic artificial intelligence
Scientific reviews are central to knowledge integration in materials science, yet their key insights remain locked in narrative text and static PDF tables, limiting reuse by humans and machines alike. This article presents a case study in atomic layer deposition and etching (ALD/E) where we publish review tables as FAIR, machine-actionable comparisons in the Open Research Knowledge Graph (ORKG), turning them into structured, queryable knowledge. Building on this, we contrast symbolic querying over ORKG with large language model-based querying and argue that a curated symbolic layer should remain the backbone of reliable neurosymbolic AI in materials science, with LLMs serving as complementary, symbolically grounded interfaces rather than standalone sources of truth.
Authors
- Riikka L. Puurunen (ORCID: https://orcid.org/0000-0001-8722-4864)
- Jennifer D’Souza (ORCID: https://orcid.org/0000-0002-6616-9509)
- Adriaan J. M. Mackus (ORCID: https://orcid.org/0000-0001-6944-9867)
- Bora Karasulu (ORCID: https://orcid.org/0000-0001-8129-8010)
- Anjana Devi (ORCID: https://orcid.org/0000-0003-2142-8105)
- Eleni Poupaki
- Erwin Kessels (ORCID: https://orcid.org/0000-0002-7630-8226)
- Sören Auer (ORCID: https://orcid.org/0000-0002-0698-2864)
Institutions
- Leibniz University Hannover (DE)
- Technische Informationsbibliothek (TIB) (DE)
- Leibniz Institute for Solid State and Materials Research (DE)
- University of Warwick (GB)
- L3S Research Center (DE)
- Eindhoven University of Technology (NL)
- Aalto University (FI)
Publication Details
- Journal
- Journal of Vacuum Science & Technology A Vacuum Surfaces and Films
- Published
- 2026-04-16
- DOI
- https://doi.org/10.1116/6.0005226
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00