AI Research Paper Analysis — Read, Summarize, Discover
Upload papers and get instant AI summaries, knowledge Q&A, and materials science computation. Built for researchers.
Scinaut is built for materials science research — literature summary, knowledge Q&A, lab assistant, and materials computation engine. From paper to publication, all in one place. Start free — no credit card required.
See it in action.
Paste any paper abstract or experiment protocol — AI analyzes it live.
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Paste text and click Analyze to see results
Summary Modes
Three ways to understand a paper
From quick extraction to deep critique — choose the lens that fits your research workflow.
Quick Overview
Core Extraction
Goals, methods, results, conclusions structured in seconds.
- Structured output
- Instant results
Deep Dive
Critical Analysis
Evaluate methodology, detect biases, assess evidence quality.
- Bias detection
- Evidence grading
Review
Study Cards
10–15 Q&A cards for exam prep and quick concept review.
- Q&A format
- 10–15 cards
Context
Follow-up Q&A
Ask anything about the paper — AI answers with citations.
- Source citations
- Multi-turn
Not Just Search
Answers. Not links.
Regular RAG gives you a pile of relevant snippets to piece together. Our synthesis layer delivers a complete, cited, self-questioning answer — like a review written by a principal scientist.
Trust
Every sentence has a source
Factual claims are followed by clickable citation badges [1][3]. Click to jump to the source document. Expert judgments marked [expert], uncertainty marked [?]. No black box — every claim is traceable.
- Inline citation badges
- Click-to-source
- Expert/uncertainty markers
Critical
Tells you what your paper doesn't
AI proactively identifies what's MISSING from your references — synthesis temperatures, characterization data, long-term stability. And tells you what to look for next. Search engines can't do this.
- Knowledge gap detection
- Severity ratings
- Remediation suggestions
Expert
Polymer? Ceramic? Semiconductor? It knows.
No domain assumptions. Upload a perovskite paper → photovoltaic framework. Upload a MOF paper → catalysis framework. Won't force battery logic onto non-battery materials. 40 years of expertise across all materials disciplines.
- 10+ materials disciplines
- Auto domain detection
- Zero preset frameworks
Preview: a real knowledge Q&A
## Expert Analysis
This work employs in-situ synthesis to prepare silicate-coated iron hydroxyphosphate for Cd(II) removal. Synthesis parameters estimated from similar literature: iron source FeCl₃·6H₂O (0.1–0.5 M), phosphorus source H₃PO₄...12expert
🔍 Knowledge Gaps
- 🟡Synthesis temperature, pH, stirring speed not reported — section "2.2 Material preparation" lacks critical parameters
- 🟡No control experiment with uncoated samples — cannot quantify coating benefit
- 🟢Tested only in deionized water — real wastewater co-ions (Ca²⁺, Mg²⁺) would severely interfere
📚 References
Not Just Another AI Chat
Built-in world-class Materials Science & Engineering AI院士 system. 40 years of experience distilled into diagnostic rules, multi-scale analysis frameworks, and real database queries — every answer is sourced, every number has a method.
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Materials Database
Direct query to Materials Project, AFLOW, OQMD. Search by formula, elements, or properties. Get crystal structures, band gaps, stability. Local caching avoids repeated API calls.
100,000+ materials · instant query
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Computation Engine
Battery voltage/capacity/energy density. Phase diagrams. Defect formation energies. Diffusion barriers. Dopant screening. Pourbaix diagrams. No DFT installation needed.
6 compute modes · sub-second response
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Knowledge Graph
Material → Process → Property → Failure relationship graph. Seeded with Li-ion, solid electrolytes, perovskite PV, electrocatalysis. Query in natural language.
50+ nodes · 60+ edges · growing
Open in Your Project、Ask or Compute、Get Verifiable Answers.
每一步都是即时的、视觉化的、零配置的 —— 用可组合的文件，而不是不透明的提示词。
Open in Your Project→
Enter any research project, click the 🔬 Materials Science tab. No setup needed.
Ask or Compute→
Search databases, run battery estimates, query the knowledge graph — or just ask materials questions in Q&A, AI auto-activates materials science skills.
Get Verifiable Answers→
Every answer cites data sources. Every computation is reproducible. Not LLM fiction. It's computed.
Comparison
Scinaut vs the alternatives
Purpose-built for research. Not a general chatbot, not a manual slog.
| Capability | Scinaut | ChatGPT | Manual Reading |
|---|---|---|---|
| Structured summaries | 3 modes built-in | Prompt engineering needed | Hours per paper |
| Source citations | Every answer cited | Hallucination risk | Manual notes |
| Critical analysis | Bias + methodology audit | Surface-level only | Requires expertise |
| Multi-document Q&A | RAG across projects | Context window limits | Cross-ref manually |
| PDF parsing | Drag & drop | Copy & paste | N/A |
| 🔬 Materials Database | 100K+ materials DFT data query | Search web manually | Check handbooks |
| ⚡ Computation Engine | Battery/doping/defect estimation | Install DFT code / can't use | Manual calc |
| 🕸️ Knowledge Graph | Material→property→failure graph | Not available | Memory-based |
| 🧪 Lab Assistant | Protocol analysis + morphology prediction | Generic unverifiable answers | Literature-based |
| Speed (per paper) | ~30 seconds | ~2 min (prompting) | 30-60 min |
FAQ
Frequently Asked Questions
What can Scinaut do?+
Upload research papers and get AI-powered summaries, critical analysis, and interactive Q&A. Built for researchers who need to process papers efficiently.
How is this different from ChatGPT?+
Scinaut has structured summary modes (extraction, analysis, study cards), built-in critical thinking, source citations, and multi-document RAG. ChatGPT requires manual prompt engineering for each of these.
What file formats are supported?+
PDF documents are fully supported. You can also paste text directly for analysis.
Is my data private?+
Yes. Your documents are stored in your own Supabase database. We do not train on your data.