Microbial electrochemical systems · research intelligence
MESSAI unifies a curated microbial-electrochemistry corpus, a causal parameter model, and a calibrated ML stack — so you can discover what’s known, design a reactor in 3D, and predict performance with honest uncertainty.
Live 3D · dual-chamber microbial electrolysis cell
What researchers can do here
Every surface reads from the same curated corpus and the same calibrated model stack — so what you discover, analyze, and predict all trace back to source.
Drive an interactive 3D microbial fuel cell and open a sourced dossier on every layer — its material, role, and the literature-traced fix a rigorous review caught — then compare it against reference designs.
Learn more →Browse canonical parameters with per-system literature distributions, hierarchical priors, and confidence — not single-number folklore.
Open the catalog →See how parameters depend on one another through a force-directed knowledge graph — fitted causal edges, correlations, and provenance in one interactive view.
Explore the graph →Build a bioelectrochemical reactor in the 3D lab and get live performance predictions with calibrated uncertainty and mechanistic loss breakdowns.
Open the lab →Fix your operating conditions, sweep one variable, and watch predictions move against the empirical distribution from the corpus.
Run a sweep →A research-scan view of physics-violating results, statistical anomalies, and Bayesian outliers worth a closer look.
Open the hunter →How it works
The same pipeline feeds every surface. Each stage is auditable — you can trace any number on the platform back through calibration, canonicalization, and extraction to the paper it came from.
20k+ MES papers ingested, with full-text PDFs content-addressed and embedded for semantic retrieval.
Two-tier LLM extraction pulls measurements with their conditions — every value keeps its DOI, snippet, and confidence.
Raw values are normalized to SI and mapped to canonical parameters, so numbers from different papers become comparable.
Hierarchical Bayesian priors (PyMC, log-normal) per system class, wrapped in conformal intervals with measured coverage.
Per-physics-family predictors return every output as {value, unit, ci_low, ci_high, confidence, source} — never a bare point estimate.
Design a reactor and see the prediction, the empirical evidence behind it, and the mechanistic loss decomposition side by side.
Calibrated prediction
Literature measurements are normalised to SI, pooled into a hierarchical log-normal prior per system class, and wrapped in conformal intervals with measured coverage. You get an interval, not folklore.
Causal structure
Parameters are linked in a directed acyclic graph. Each edge is labelled by how it was established — derived from physics, or fitted from co-observations in the corpus — so you can tell a mechanism from a correlation.
Open by default
The data and tooling that power MESSAI are released as standalone, citable packages — each with a schema, a license, and a CITATION.cff. Use them in your own analysis; they don’t depend on the platform.
The canonical MES parameter ontology — variables with literature distributions, a causal DAG, and full provenance.
CC BY 4.0DFT-computed electrode, membrane, and catalyst properties with Materials Project provenance and Pourbaix stability.
CC BY 4.0Curated MES-relevant microorganisms — electrogens, electrotrophs, community partners, and negative controls.
MITMethodology tooling and a physics-constraint validator for experimental design, sample sizing, and QC (Python).
MITPhysics-based models — Butler-Volmer kinetics, biofilm growth, and Nernst / Monod calculators.
MITEducational calculators — sustainability, life-cycle assessment, techno-economics, and SDG alignment.
CC BY 4.0A multi-agent research orchestration framework for literature analysis and insight generation.
MITResearch-gap identification and hypothesis generation with confidence scoring and contradiction detection.
MITA slug-keyed catalog of curated open MES datasets (Zenodo, Figshare), cross-referenced to Parameters and Materials.
CC BY 4.0New to the packages? Start with the manual or read how the parameter package is consumed downstream.
Two front doors · one platform
If you operate a waste stream
Pick a waste stream → see predicted recovery
Six industrial archetypes + custom influent characterisation. Live ML predictions with 95% CI.
Honest TEA + LCA per archetype
CapEx, OpEx, LCOE, LCO-water scaled with flow. GWP + fossil energy from peer-reviewed baselines.
5-physics-family routing
MFC / MEC / MDC / MES / MNRC / MMRC / MBES auto-routed by influent characteristics.
If you research MES
23,480 papers · 195,846 measurements
Two-tier extraction (paper header + per-measurement conditions). Every row carries DOI + snippet + confidence.
706 canonical parameters, FK-linked DAG
2,812 mechanistic edges with literature support. Multi-axis taxonomy (17 primary types × subtypes × combinations).
Per-class predictors + conformal bounds
97.98% OOS coverage at the 95% target on 940 held-out observations. Hierarchical priors fit via PyMC NUTS.
Get started
Search the corpus, design a reactor, and get predictions that carry their own uncertainty and cite their own sources.