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This paper addresses the fragmentation of Atomic Layer Deposition (ALD) and Etching literature by introducing standardized JSON schemas to enable machine-actionable data extraction and comparison. By structuring experimental and simulation records, these tools facilitate the integration of materials science datasets into larger AI-driven discovery workflows, directly supporting the development of robust scientific models.
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Mined from Scientific Literature: Process Schemas for Atomic Layer Deposition and Etching in Materials Science

arXiv1 min read

arXiv:2609.12139v1 Announce Type: new Abstract: Atomic layer deposition (ALD) and atomic layer etching (ALE) are reported heterogeneously across experimental and simulation literature in materials science, hindering comparison and machine-actionable reuse. We present four domain-expert-reviewed JSON Schemas for ALD and ALE experimental and simulation processes. Curated with schema-miner and grounded in QUDT using schema-miner pro, the schemas structure materials, process conditions, configurations , and measured or predicted results. We compare their scope, structure, and semantic grounding, and demonstrate their use for schema-guided literature extraction and publication of structured records through ORKG templates.

Abstract:Atomic layer deposition (ALD) and atomic layer etching (ALE) are reported heterogeneously across experimental and simulation literature in materials science, hindering comparison and machine-actionable reuse. We present four domain-expert-reviewed JSON Schemas for ALD and ALE experimental and simulation processes. Curated with schema-miner and grounded in QUDT using schema-miner pro, the schemas structure materials, process conditions, configurations , and measured or predicted results. We compare their scope, structure, and semantic grounding, and demonstrate their use for schema-guided literature extraction and publication of structured records through ORKG templates.

Artificial Intelligence (cs.AI); Materials Science (cond-mat.mtrl-sci)

arXiv:2609.12139 [cs.AI]

(or arXiv:2609.12139v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.12139 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 183 words

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