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Publishing FAIR and machine-actionable reviews in materials science: The case for symbolic knowledge in neuro-symbolic artificial intelligence

Year: 2026

Type: article

Abstract: 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.

Source: Journal of Vacuum Science & Technology A Vacuum Surfaces and Films

DOI: 10.1116/6.0005226

Authors: Jennifer D’Souza, Sören Auer, Eleni Poupaki, Alex Watkins, Anjana Devi, Riikka L. Puurunen, Bora Karasulu, Adriaan Mackus, Erwin Kessels

Institutions: Technische Informationsbibliothek (TIB), Leibniz University Hannover, L3S Research Center, Eindhoven University of Technology, University of Warwick, Leibniz Institute for Solid State and Materials Research, Aalto University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W7154632457

FWCI:

Cites: 27

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