arXiv subjects

Bora Karasulu

Publications and source records attributed to Bora Karasulu.

5 recordsLinked to original sources

arXiv2026

Mined from Scientific Literature: Process Schemas for Atomic Layer Deposition and Etching in Materials Science

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.

cs.AI↗

arXiv2026

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.

cs.AI↗

arXiv2025

LLMs4SchemaDiscovery: A Human-in-the-Loop Workflow for Scientific Schema Mining with Large Language Models

Extracting structured information from unstructured text is crucial for modeling real-world processes, but traditional schema mining relies on semi-structured data, limiting scalability. This paper introduces schema-miner, a novel tool that combines large language models with human feedback to automate and refine schema extraction. Through an iterative workflow, it organizes properties from text, incorporates expert input, and integrates domain-specific ontologies for semantic depth. Applied to materials science--specifically atomic layer deposition--schema-miner demonstrates that expert-guided LLMs generate semantically rich schemas suitable for diverse real-world applications.

cs.CL↗

arXiv2022

Carbon phase diagram with empirical and machine learned interatomic potentials

In the present work we detail how the many-body potential energy landscape of interatomic potentials for carbon can be explored by utilising the nested sampling algorithm, allowing the calculation of their pressure-temperature phase diagram up to high pressures. We present a comparison of three interatomic potential models, Tersoff, EDIP and GAP-20, focusing on their macroscopic properties, particularly on their melting transition and on identifying thermodynamically stable solid structures up to at least 100 GPa. The studied models all form graphite structures upon freezing at lower pressure, then the diamond structure as the pressure increases. We were able to locate the transition between these phases in case of the Tersoff and EDIP models. We placed particular focus on the state-of-the-art machine learning (ML) model, GAP-20, and calculated its phase diagram up to 1 TPa to evaluate its predictive capabilities well outside of the model's fitting conditions. The phase diagram showed a remarkably good agreement with the experimental phase diagram up to 200 GPa, despite a variety of unexpected graphite layer spacing. Above that nested sampling identified two novel stable solid structures, a strained diamond structure and above 800 GPa a strained hexagonal-close-packed structure. However, the stability of these two phases were not confirmed by DFT calculations, highlighting potential routes to further improve the ML model.

cond-mat.mtrl-sci↗

arXiv2020

Boron phosphide as a \emph{p}-type transparent conductor: optical absorption and transport through electron-phonon coupling

Boron phosphide has recently been identified as a potential high hole mobility transparent conducting material. This promise arises from its low hole effective masses. However, BP has a relatively small 2 eV indirect band gap which will affect its transparency. In this work, we computationally study both optical absorption across the indirect gap and phonon-limited electronic transport to quantify the potential of boron phosphide as a \emph{p}-type transparent conductor. We find that phonon-mediated indirect optical absorption is weak in the visible spectrum and that the phonon-limited hole mobility is very high (around 900 cm$^2$/Vs) at room temperature. This exceptional mobility comes from a combination of low hole effective mass and very weak scattering by polar phonon modes. We rationalize the weak scattering by the less ionic bonding in boron phosphide compared to oxides. We suggest this could be a general advantage of non-oxides for \emph{p}-type transparent conducting applications. Using our computed properties, we assess the transparent conductor figure of merit of boron phosphide and shows that it exceeds by one order of magnitude that of established \emph{p}-type transparent conductors, confirming the potential of this material.

cond-mat.mtrl-sci↗