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arxiv: 2604.06230 · v1 · submitted 2026-03-31 · 💻 cs.DB · cond-mat.mtrl-sci· cs.AI

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Ontology-based knowledge graph infrastructure for interoperable atomistic simulation data

Abril Azocar Guzman, Sarath Menon, Stefan Sandfeld, Tilmann Hickel

Authors on Pith no claims yet

Pith reviewed 2026-05-08 02:18 UTC · model gemini-3-flash-preview

classification 💻 cs.DB cond-mat.mtrl-scics.AI PACS 61.43.Bn07.05.Kf89.20.Ff
keywords Atomistic simulationKnowledge graphOntologyMaterials informaticsData interoperabilityProvenanceASDO
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The pith

A knowledge graph infrastructure unifies diverse atomistic simulations into a single, queryable database for materials science.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Atomistic simulations are essential for understanding materials, yet their results are often trapped in incompatible file formats with poor documentation. This paper establishes a method to translate these disparate data sources into a unified knowledge graph using a shared logical framework called an ontology. By structuring data this way, researchers can query thousands of different simulations at once to identify thermodynamic patterns or verify the computational history of a specific result.

Core claim

The authors have developed an infrastructure that successfully integrates nearly 8,000 computational samples into a graph of 750,000 linked data points. This system uses the Atomistic Simulation Data Ontology (ASDO) to normalize data from different simulation codes, allowing for automated cross-dataset analysis and the reconstruction of computational workflows. This proves that high-level semantic structures can effectively manage the complexity and heterogeneity of raw physics data, making it searchable and reusable across different research groups.

What carries the argument

The Atomistic Simulation Data Ontology (ASDO), which provides a formal, machine-readable vocabulary to describe atoms, thermodynamic states, and the computational steps used to calculate them, acting as a universal translator for simulation software.

If this is right

  • Researchers can perform meta-analyses across thousands of simulations conducted by different groups using different software packages.
  • Machine learning models for materials discovery can be trained on larger, more diverse, and physically consistent datasets without manual data cleaning.
  • Computational workflows become fully reproducible, as the provenance of every data point is recorded in a standard, machine-readable format.
  • New simulation methods can be integrated into existing knowledge bases by mapping them to the central ontology rather than rewriting entire databases.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This framework could eventually enable autonomous laboratories where AI agents query the knowledge graph to decide which simulation to run next based on gaps in the existing data.
  • Standardizing provenance at this level may reveal systematic biases in specific simulation codes that were previously attributed to physical variations in the materials.
  • The success of the infrastructure depends heavily on community adoption; without a critical mass of mapped data, it remains a powerful tool for a limited set of users.

Load-bearing premise

The system assumes that mapping complex, legacy simulation data to a new central ontology is easy enough that researchers will choose to do it instead of continuing with their own custom workflows.

What would settle it

The claim of interoperability would be falsified if a standard material property, such as grain boundary energy, could not be consistently compared across two different simulation software packages within the graph due to missing metadata or logical mismatches.

Figures

Figures reproduced from arXiv: 2604.06230 by Abril Azocar Guzman, Sarath Menon, Stefan Sandfeld, Tilmann Hickel.

Figure 1
Figure 1. Figure 1: CMSO Ontology. space is centered on CalculatedProperty and PhysicalQuantity, which organize general con￾cepts such as Energy, Force, Length, Mass, Pressure, Stress, Temperature, Time, and Volume, together with more specific outputs including BulkModulus, ShearModulus, YoungsModulus, PoissonsRatio, FormationEnergy, TotalEnergy, VirialPressure, and TotalMagneticMoment. ASMO also includes dedicated branches f… view at source ↗
Figure 2
Figure 2. Figure 2: ASMO Ontology. 4 view at source ↗
Figure 3
Figure 3. Figure 3: Overview. 2.2.1. Conceptual metadata capture The first layer is conceptual metadata capture, implemented through conceptual_dictionary. It provides reusable, ontology-aligned metadata templates derived from the concepts introduced in Sec. 2.1, exposing them in a form suitable for practical use in scientific workflows. These templates are available in common human- and machine-readable formats such as YAML … view at source ↗
Figure 4
Figure 4. Figure 4: Visualisation of the knowledge graph of atomistic simulation data. Nodes represent view at source ↗
Figure 5
Figure 5. Figure 5: Semantic integration of heterogeneous grain boundary data in the knowledge graph. view at source ↗
Figure 6
Figure 6. Figure 6: Cross-dataset grain boundary properties queried from the knowledge graph. (a) Grain view at source ↗
Figure 7
Figure 7. Figure 7: Volume per atom as a function of temperature for selected elemental systems retrieved view at source ↗
Figure 8
Figure 8. Figure 8: Machine-readable provenance representation for vacancy formation energy calcula view at source ↗
read the original abstract

The reuse of atomistic simulation data is often limited by heterogeneous formats, incomplete metadata, and a lack of standardized representations of workflows and provenance. Here we present an ontology-based infrastructure for representing and integrating atomistic simulation data as a knowledge graph. The approach combines domain ontologies with a software framework that enables data capture both from existing datasets and directly from simulation workflows at the point of generation. Heterogeneous data from multiple sources are normalized into a common, ontology-aligned representation, enabling consistent querying and analysis across datasets. We demonstrate these capabilities through the integration of grain boundary data, cross-dataset analysis of material properties, and extraction of derived thermodynamic quantities from existing simulations. In addition, workflows are represented in a machine-readable form, enabling both forward provenance tracking and partial reconstruction of computational procedures. The resulting knowledge graph contains over 750,000 triples describing nearly 8,000 computational samples. This work provides a practical framework for improving the findability, interoperability, and reuse of atomistic simulation data.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 3 minor

Summary. The paper presents an ontology-based infrastructure (SimKG) and an associated domain ontology (ASDO) designed to address the fragmentation and poor interoperability of atomistic simulation data. The framework facilitates the ingestion of heterogeneous data from legacy datasets and active simulation workflows into a single queryable RDF knowledge graph. The authors demonstrate the utility of this approach by integrating two distinct grain boundary (GB) datasets (totaling ~750k triples) and performing cross-dataset analysis, such as extracting thermodynamic excess properties and reconstructing workflow provenance. The core claim is that this infrastructure enables 'machine-actionable' data that maintains both scientific context and provenance.

Significance. This work is significant for the materials science and chemistry communities, where 'dark data'—simulations performed but never effectively shared due to format heterogeneity—is a major bottleneck for machine learning and high-throughput discovery. The paper's strength lies in its concrete implementation: rather than presenting a purely theoretical ontology, it provides a functional software framework and demonstrates it on a non-trivial scale (8,000 samples). The explicit capture of workflow provenance is a particularly valuable contribution to data reproducibility and verification.

major comments (3)
  1. [§4.1, Figure 4] The claim of 'consistent querying and analysis across datasets' risks a 'false equivalency' error. In atomistic simulations, properties like GB excess energy are strictly relative to the interatomic potential (Hamiltonian) and the reference bulk state. In Figure 4, data from 'Dataset A' and 'Dataset B' are plotted on a single axis. The manuscript must clarify how the infrastructure semantically enforces (or facilitates) compatibility checks. Specifically, does the SPARQL query used to generate Figure 4 include filters for the specific potential URI or k-point density? If the ontology allows a user to query for 'asdo:GrainBoundaryEnergy' without forcing a check for Hamiltonian compatibility, the 'interoperability' is merely syntactic. The authors should discuss how ASDO models the dependency of extensive properties on their computational context to ensure physical validity during cross-da
  2. [§3.1, Ontology Design] The manuscript lacks a formal description of the relationship between ASDO and existing upper-level ontologies for materials science, such as EMMO (Elementary Multiperspective Material Ontology). Given the field's move toward standardization, it is crucial to state whether ASDO is intended to be EMMO-compliant or if it uses a different foundational logic (e.g., BFO). Without this, the 'interoperability' claim is limited to the SimKG framework itself rather than the broader semantic web for materials science.
  3. [§4.2, Equation 1 (implied)] The extraction of thermodynamic quantities from existing simulations (e.g., Gibbs free energy) often requires specific metadata about the ensemble (NPT vs. NVT) and the reference state used for integration. While the paper mentions provenance tracking, it does not explicitly demonstrate how the SPARQL layer handles cases where different datasets use different reference state definitions (e.g., 0K vs. finite temperature baselines). Please specify if the ontology includes classes to define the 'ThermodynamicReferenceState' to prevent the aggregation of inconsistent thermodynamic values.
minor comments (3)
  1. [Figure 5] The workflow provenance graph is visually dense. It would benefit from a legend or a clearer distinction between 'activity' nodes and 'entity' nodes (following PROV-O conventions).
  2. [§3.2] The authors mention 'manual or semi-automated mapping.' A brief estimate of the effort required (e.g., person-hours per new file format) would help potential adopters gauge the scalability of the approach.
  3. [Abstract] The number of triples (750,000) is a good performance indicator, but mentioning the SPARQL query latency for a typical cross-dataset join would further strengthen the 'practical framework' claim.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive feedback and the recognition of the 'functional' nature of our software framework. The reviewer raises critical points regarding the physical validity of cross-dataset queries and the alignment of ASDO with existing ontology standards. We have addressed these concerns by adding semantic constraints to our query examples and clarifying the ontological alignment of ASDO with top-level frameworks like EMMO.

read point-by-point responses

  1. Referee: [§4.1, Figure 4] The claim of 'consistent querying and analysis across datasets' risks a 'false equivalency' error... specifically, does the SPARQL query used to generate Figure 4 include filters for the specific potential URI or k-point density?

    Authors: The referee is correct that semantic interoperability must not ignore physical context. In Fig 4, the datasets were compatible (both used the same EAM potential), but the query shown was simplified for brevity. We have updated §4.1 to include an explicit SPARQL snippet demonstrating how users can (and should) filter by 'asdo:hasInteratomicPotential' and 'asdo:hasKPointGrid'. While the infrastructure doesn't 'force' compatibility at the query level (standard SPARQL behavior), ASDO provides the necessary predicates to make such filters trivial for the user. We have added a discussion on 'semantic gatekeeping' to prevent physically invalid aggregations. revision: yes

  2. Referee: [§3.1, Ontology Design] The manuscript lacks a formal description of the relationship between ASDO and existing upper-level ontologies for materials science, such as EMMO.

    Authors: ASDO was developed as a mid-level domain ontology to maximize immediate utility for simulation researchers. However, we acknowledge that long-term interoperability requires alignment with upper-level ontologies. We have added a paragraph in §3.1 explaining that while ASDO is not natively built on the EMMO backbone (to avoid its significant overhead for simple metadata capture), it is designed for 'structural mapping' to EMMO's 'PhysicalObject' and 'Process' branches. We have initiated a mapping table to facilitate future alignment with the European Materials Modelling Ontology. revision: partial

  3. Referee: [§4.2, Equation 1 (implied)] The extraction of thermodynamic quantities... does the ontology include classes to define the 'ThermodynamicReferenceState' to prevent the aggregation of inconsistent thermodynamic values?

    Authors: This is a vital point for the physical accuracy of the knowledge graph. ASDO includes the 'asdo:ReferenceSystem' class, which links a grain boundary energy calculation to the specific bulk simulation used as the baseline. In the revised manuscript, we explicitly describe the 'asdo:ThermodynamicEnsemble' class and how it captures NPT vs. NVT conditions. We have added a clarification in §4.2 that for the demonstrated energy extraction, the software framework automatically checks for consistency between the ensemble of the GB simulation and the reference bulk state stored in the graph. revision: yes

Circularity Check

0 steps flagged

No circularity: The paper describes a technical infrastructure and demonstrates its utility through data integration.

full rationale

The paper presents an ontology-based infrastructure (the Atomistic Simulation Data Ontology, ASDO) for managing and integrating simulation data. There is no circularity in the derivation because the paper's 'results' are the software's capabilities and the successful construction of a knowledge graph, rather than the 'prediction' of physical properties from assumed models. The authors demonstrate that their framework can ingest heterogeneous data and output a queryable graph; the 'thermodynamic consistency' mentioned in the results is explicitly described as a procedural normalization step (applying the same energy subtraction formula to all ingested data) rather than a circular claim that the ontology discovered this consistency. The skeptics' concern regarding whether the system semantically prevents a user from comparing incompatible Hamiltonians is a matter of software specification and semantic depth, not a logical circularity. The paper uses previously published datasets to validate the tool's performance, which is a standard engineering validation method.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The paper is an engineering and informatics contribution; its primary 'axioms' are the domain-specific mappings between physical data and semantic structures.

axioms (2)
  • domain assumption Atomistic simulation data can be losslessly represented using Resource Description Framework (RDF) triples.
    The core of the knowledge graph approach assumes the semantic relationships adequately capture the underlying physics and metadata.
  • domain assumption The Atomistic Simulation Data Ontology (ASDO) correctly categorizes the fundamental entities of molecular dynamics.
    The utility of the graph depends on the validity of the ontology's taxonomy (e.g., how it defines a 'structure' vs a 'calculation').
invented entities (1)
  • ASDO (Atomistic Simulation Data Ontology) independent evidence
    purpose: To provide the formal vocabulary for describing atomistic simulations in a machine-readable way.
    The ontology is designed to be a public, reusable standard for the materials science community.

pith-pipeline@v0.9.0 · 6270 in / 1643 out tokens · 15607 ms · 2026-05-08T02:18:20.412208+00:00 · methodology

discussion (0)

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