MaterialGraph

Graph-Based Material Intelligence Platform for Battery Material Candidate Screening, Risk Analysis, and Decision Support

Overview

MaterialGraph is a graph-based material intelligence platform designed to support candidate screening, risk-aware ranking, substitution analysis, and scenario-driven decision support for battery materials.

The platform integrates scientific material datasets with supply-risk intelligence to help answer questions such as:

Given lithium or cobalt scarcity, which alternative battery material candidates remain attractive under defined constraints?

MaterialGraph is intentionally designed as a decision-support platform, not a material discovery system.

The platform evaluates and compares known material candidates using explainable scoring, risk models, graph relationships, and scenario analysis.

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Quick Start

git clone https://github.com/<username>/materialgraph.git
cd materialgraph

python -m venv .venv
pip install -r requirements.txt

alembic upgrade head

python scripts/import_materials_project.py

uvicorn app.main:app --reload

Key Features

Materials Project Integration

• Import real battery material candidates from Materials Project
• Store computed material properties
• Preserve raw source metadata
• Upsert materials and element relationships

Material-Element Graph

Model relationships between:

• Materials
• Elements
• Applications
• Risk Profiles

Graph relationships include:

• Material → Element
• Material → Application
• Element → Risk Profile

Risk Intelligence

Aggregate element-level risk information into material-level risk scores.

Current risk dimensions:

• Supply Risk
• Geopolitical Risk
• Toxicity
• Abundance

Candidate Screening

Evaluate materials under configurable constraints:

• Scarce elements
• Avoided elements
• Stability requirements
• Energy-above-hull limits

Example:

Lithium Scarcity
Avoid Cobalt
Require Stable Candidates

Candidate Comparison

Directly compare two materials.

Example:

Na3Fe(PO4)2
vs
Na2Mn2O3

Outputs:

• Screening scores
• Risk scores
• Winner selection
• Explainable reasoning

Scenario Ranking

Evaluate candidate rankings under strategic scenarios.

Examples:

• Lithium Supply Shock
• Cobalt Restriction
• Critical Material Constraints

Sensitivity Analysis

Analyze ranking sensitivity under changing risk conditions.

Examples:

• +25% supply risk
• +50% supply risk
• +25% geopolitical risk
• +50% geopolitical risk

Substitution Analysis

Identify potential substitutes for a material candidate.

Example:

LiFePO4
→ NaFePO4
→ Na3Fe(PO4)2

Using:

• Composition similarity
• Material risk
• Shared chemistry
• Explainable substitution reasoning

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Technology Stack

Backend

• Python
• FastAPI
• PostgreSQL
• SQLAlchemy
• Alembic
• NetworkX
• Pydantic v2

Testing

• pytest

Data Sources

Current:

• Materials Project

Future:

• USGS Mineral Commodity Summaries
• Scientific Literature Sources
• Industrial Supply Chain Datasets

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Architecture

Materials Project
        ↓
Material Import
        ↓
Material Graph
        ↓
Element Risk Profiles
        ↓
Material Risk Scoring
        ↓
Candidate Screening
        ↓
Candidate Comparison
        ↓
Scenario Ranking
        ↓
Sensitivity Analysis
        ↓
Substitution Analysis

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Current Data Model

Material

Represents battery material candidates.

Examples:

• LiFePO4
• NaFePO4
• NaMnO2
• MgMn2O4

Element

Represents chemical elements.

Examples:

• Li
• Na
• Mg
• Fe
• Mn
• O

MaterialElement

Associative relationship between materials and elements.

Application

Target application domain.

Examples:

• Battery Cathode
• Battery Anode
• Solid Electrolyte

RiskFactor

Risk categories used for evaluation.

ElementRiskProfile

Element-level risk intelligence.

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Example Workflow

Candidate Screening

Request:

{
  "scarce_elements": ["Li"],
  "avoid_elements": ["Co"],
  "require_stable": true,
  "max_energy_above_hull": 0.05
}

MaterialGraph:

• Evaluates candidates
• Applies penalties
• Computes risk-aware scores
• Returns ranked candidates

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Example Questions

MaterialGraph is designed to answer:

Screening

Which battery material candidates remain attractive under lithium scarcity?

Comparison

Why is candidate A better than candidate B?

Scenario Analysis

How do rankings change when cobalt becomes constrained?

Sensitivity Analysis

How sensitive is a candidate to worsening supply risk?

Substitution Analysis

If LiFePO4 becomes unattractive, what should I consider instead?

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Current Status

Phase 1 — Candidate Screening Core

Completed:

• FastAPI foundation
• PostgreSQL integration
• SQLAlchemy models
• Alembic migrations
• Materials Project importer
• Material graph foundation
• Risk-aware screening engine
• Candidate comparison
• Scenario ranking
• Sensitivity analysis
• Substitution analysis
• Service tests
• API tests

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Project Scope

MaterialGraph does not:

• Perform autonomous material discovery
• Replace computational chemistry workflows
• Replace DFT calculations
• Guarantee synthesis feasibility
• Provide laboratory validation

MaterialGraph focuses on:

• Candidate exploration
• Risk-aware reasoning
• Substitution analysis
• Decision support
• Graph-based material intelligence

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Future Roadmap

Phase 2

• USGS integration
• Criticality analysis
• Supply concentration intelligence
• Material family exploration
• Similarity search
• Enhanced graph relationships

Phase 3

• Graph analytics
• Graph embeddings
• Recommendation systems
• Machine-assisted candidate exploration

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License

MIT License