A research prototype demonstrating grounded LLM agents over a formal RDF/OWL knowledge graph for photovoltaic (PV) solar energy and materials science. Every answer is traceable to specific SPARQL-retrieved triples — no hallucinations, full provenance.
SolarGraph AI combines a hand-crafted OWL ontology, a SPARQL query engine, and a multi-step ReAct agent to answer domain questions about PV materials. The system is designed around three core principles from the materials informatics literature:
- Grounding: the LLM is constitutionally constrained to KG-retrieved facts only
- Tool use: a ReAct loop lets the agent iteratively run SPARQL queries and reflect on results before answering
- Provenance: every answer records the cited entities, supporting triples, and SPARQL queries that produced it
This directly mirrors the workflow described in emerging materials science LLM agent systems, where structured knowledge retrieval replaces unstructured vector search to enable reproducible, auditable answers.
User Question (natural language)
│
▼
┌───────────────────────────────────┐
│ ReAct Agent (Groq LLM) │
│ │
│ while not done: │
│ Thought → select tool │
│ Action → call SPARQL tool │
│ Observation → inspect results │
│ Final Answer + Provenance Record │
└──────────────┬────────────────────┘
│ SPARQL (RDFLib)
▼
┌───────────────────────────────────┐
│ PV Solar OWL Ontology │
│ 13 classes · 13 object props │
│ 8 data props · 70+ individuals │
│ Turtle/RDF · 724 lines │
└───────────────────────────────────┘
Also included: a fast single-shot agent with dual-layer caching (LRU + JSON file) for low-latency repeated queries.
| OWL Class | # Individuals | Key Examples |
|---|---|---|
| Absorber | 8 | c-Si (1.12 eV), MAPbI₃ (1.55 eV), FAPbI₃ (1.48 eV), CIGS (1.15 eV), CdTe (1.44 eV) |
| CellArchitecture | 12 | PERC 24.5%, TOPCon 26.1%, SHJ 26.8%, Perovskite/Si Tandem 33.9% |
| FabricationProcess | 13 | Czochralski, PECVD, Spin Coating, Slot-Die, Co-Evaporation |
| CharacterisationTechnique | 10 | J-V, EQE, TRPL, XRD, SEM, TEM, DLTS, PL, EL |
| Defect | 7 | Iodide Vacancy, Grain Boundary Traps, Phase Separation (α→δ FAPbI₃) |
| PerformanceMetric | 6 | PCE, Voc, Jsc, FF, Carrier Lifetime, Hysteresis Index |
| DegradationMechanism | 5 | Moisture Ingress, Thermal Degradation, Ion Migration, PID |
| Institution | 8 | NREL, Fraunhofer ISE, HZB, KAUST, EPFL, Oxford PV |
| Researcher | 5 | Grätzel, Snaith, Miyasaka, Sargent, Bein |
| Feature | Implementation |
|---|---|
| OWL ontology | 13 classes, subclass hierarchy, domain/range constraints, rdfs:label/comment |
| SPARQL engine | 15+ domain-specific query methods via RDFLib |
| Fast agent | Single-shot RAG: SPARQL context → Groq LLM → answer |
| ReAct agent | Multi-step tool-use loop with up to 6 iterations |
| Provenance | Entity detection + triple lookup + SPARQL audit trail per answer |
| Dual-layer cache | functools.lru_cache (in-process) + JSON file (24h TTL) |
| Graph visualiser | Self-contained vis.js CDN network — no 404s |
| REST API | /api/entities, /api/absorbers, /api/architectures, /api/search |
| Gradio UI | HuggingFace Spaces-compatible interface |
# Subclass hierarchy
pv:Absorber rdfs:subClassOf pv:Semiconductor .
pv:Semiconductor rdfs:subClassOf pv:Material .
# Object property with domain/range
pv:hasDefect a owl:ObjectProperty ;
rdfs:domain pv:Semiconductor ;
rdfs:range pv:Defect .
# Individual with typed literals and relationships
pv:MAPbI3 a pv:Absorber ;
pv:name "Methylammonium Lead Iodide (MAPbI3)" ;
pv:bandgap_eV "1.55"^^xsd:decimal ;
pv:crystalStructure "Cubic ABX3 perovskite" ;
pv:hasDefect pv:IodideVacancy, pv:GrainBoundaryTrap ;
pv:fabricatedBy pv:SpinCoating, pv:SlotDieCoating ;
pv:characterisedBy pv:XRD, pv:PL, pv:TRPL, pv:SEM .Prerequisites: Python 3.10+ · Free Groq API key
git clone https://github.com/YOUR_USERNAME/solargraph-ai.git
cd solargraph-ai
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env # add GROQ_API_KEY
python build_graph.py # parses ontology.ttl → graph.pkl
python app.py # → http://127.0.0.1:5000After any change to
ontology.ttl, deletegraph.pklandcache.jsonbefore restarting.
# 1. Create a new Space at huggingface.co/new-space
# SDK: Gradio | Visibility: Public
# 2. Push your files
git remote add hf https://huggingface.co/spaces/YOUR_USERNAME/solargraph-ai
git push hf main
# 3. Add your API key
# Space Settings → Secrets → New Secret
# Name: GROQ_API_KEY Value: <your key>
# HuggingFace will auto-detect hf_app.py and launch it| Method | Endpoint | Description |
|---|---|---|
POST |
/ask |
Fast grounded answer (cached) |
POST |
/ask/react |
ReAct agent answer + provenance |
GET |
/graph |
Interactive vis.js knowledge graph |
GET |
/api/stats |
Triple + entity counts |
GET |
/api/entities?type=Absorber |
Entities by OWL class |
GET |
/api/absorbers |
Absorbers with bandgap data |
GET |
/api/architectures |
Cell architectures by efficiency |
GET |
/api/search?q=perovskite |
Full-text search |
GET |
/api/cache/stats |
Hit/miss statistics |
POST |
/api/cache/clear |
Invalidate all caches |
- OpenAlex literature ingestion pipeline (LLM entity extraction → graph)
- W3C SPARQL 1.1 endpoint via SPARQLWrapper
- Ontology alignment with EMMO, MatOnto, BattINFO
- DFT/MD simulation data as typed RDF literals
- LangGraph-based workflow orchestration
- Evaluation benchmark: answer accuracy vs. ground-truth triples
This project prototype implements the core techniques now appearing in materials science LLM research:
- Semantic data modelling — OWL/RDF encodes expert domain knowledge as machine-readable facts
- Structured RAG — SPARQL retrieval replaces unstructured vector search for reproducibility
- Agentic tool use — ReAct loop demonstrates agent control beyond single-prompt engineering
- Provenance/traceability — every answer is auditable back to specific KG triples
- Heterogeneous data integration — architecture supports connecting to simulation databases, literature, and experimental repositories
Applicable to: NOMAD, Materials Project, OPTIMADE, AFLOW, and emerging perovskite/battery knowledge graph initiatives.
| Layer | Technology |
|---|---|
| Ontology | OWL 2 / Turtle RDF |
| Graph engine | RDFLib 7 + SPARQL 1.1 |
| LLM provider | Groq API (llama3-70b-8192) |
| Agent framework | Custom ReAct loop |
| Web framework | Flask 3 |
| UI: web | Jinja2 + vanilla JS + vis.js |
| UI: HuggingFace | Gradio 4 |
| Caching | lru_cache + JSON file |
MIT — see LICENSE
@software{solargraph_ai_2026,
title = {SolarGraph AI: LLM Agent and Knowledge Graph for PV Materials Science},
author = {Whyte Goodfriend},
year = {2026},
url = {https://github.com/marblehub/solargraph-ai}
}