From Hypothesis
to Atomic Structure
Unified access to scientific literature, materials databases, DFT compute, and an AI research assistant — designed for materials scientists.
The Problem
Research Shouldn't Require
15 Open Tabs
Modern materials research demands jumping between literature databases, property repositories, simulation tools, and analysis software — each with its own interface, format, and workflow.
Hours of context-switching. Data trapped in incompatible formats. Manual copy-paste from paper tables to spreadsheets. Critical insights lost between tools.
material.codes connects your entire research stack into a single, AI-powered workflow.
Core Platform
Four Modules. One Workflow.
Every tool a materials scientist needs — tightly integrated so results from one module flow directly into the next.
Literature Intelligence
Semantic search across 2M+ indexed papers from Semantic Scholar and OpenAlex. Extract structured data, compare experimental results, and get AI-synthesized summaries with citations.
"perovskite solar cell efficiency above 25% — last 2 years"Materials Datasets
Unified access to Materials Project, AFLOW, OQMD, and Citrination. Query by composition, structure, or property. Download structures in any format for downstream simulation.
"cubic perovskites with bandgap 1.2–1.8 eV, stable at 300K"Compute Engine
Submit DFT calculations to Quantum ESPRESSO or ABINIT without writing input files. Automated pseudopotential selection, k-point grids, and convergence testing. Results parsed automatically.
"optimize geometry of mp-2815 with PBE+U, U=3.5 on Fe"AI Assistant
A domain-aware research assistant that orchestrates all four modules in response to natural language queries. Maintains conversation context and cites primary sources.
"compare Li-ion cathode candidates for high-temp applications"Live Demo
Watch It Work
Ask a research question in plain English. material.codes orchestrates the full pipeline automatically.
Animation loops automatically · Actual latency depends on compute queue
Open by Design
MCP Protocol Support
material.codes implements the Model Context Protocol (MCP), letting you connect our data tools directly to Claude, Cursor, or any MCP-compatible AI client.
Query Materials Project, run literature searches, or launch DFT jobs from inside your own AI workflows — without leaving your tool.
{ "mcpServers": { "material-codes": { "url": "https://mcp.material.codes/sse", "transport": "sse", "headers": { "Authorization": "Bearer YOUR_API_KEY" } } } }
Available tools: search_literature · query_materials · run_dft · get_properties
Connected Sources
Built on Real Data
material.codes indexes and unifies the most trusted sources in computational materials science.
Research Scenarios
Built for Real Research Questions
From high-throughput screening to targeted property engineering — see how material.codes handles complex research workflows.
Battery Material Screening
Identify promising Li-ion cathode candidates from first-principles data with application-specific voltage and capacity constraints.
"Find layered oxide cathodes with average voltage > 3.8V, capacity > 180 mAh/g, and thermal stability above 200°C"- Queries Materials Project + AFLOW for matching compositions
- Cross-references electrochemical data from literature
- Ranks candidates by synthesizability scores
- Exports structures for DFT geometry relaxation
Top Result
LiNi0.8Mn0.1Co0.1O2 (NMC811)
Pipeline Steps
Semiconductor Band Engineering
Design alloy compositions to hit target band gaps for photovoltaic and LED applications using high-throughput DFT screening.
"InGaN alloys with direct bandgap between 2.6 and 3.0 eV for blue LED emission, lattice-matched to GaN substrate"- Retrieves composition-property relationships from AFLOW
- Applies Vegard's law interpolation with bowing corrections
- Generates band structure plots from existing calculations
- Flags lattice mismatch strain for selected compositions
Optimal Composition
In0.18Ga0.82N
Screening Results
Catalyst Surface Analysis
Identify active sites, compute adsorption energies, and screen surface terminations for heterogeneous catalysis applications.
"CO2 reduction on Cu surfaces — compare (100), (110), (111) facets for formate vs CO selectivity"- Constructs surface slabs from bulk structure
- Computes surface energies and identifies stable terminations
- Submits NEB calculations for reaction barriers
- Correlates results with selectivity trends from literature
Recommended Facet
Cu(100) — formate pathway
Adsorption Energies (eV)
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