Microbial electrochemical systems · research intelligence
MESSAI unifies a curated microbial-electrochemistry corpus, a parameter graph built on domain consensus, and calibrated prior intervals — so you can discover what’s known, design a reactor in 3D, and see literature-based performance ranges with their conditions and limits.
Live 3D · Drag to rotate · dual-chamber microbial electrolysis cell
What researchers can do here
Every surface reads from the same curated corpus and the same prior stack — so what you discover, analyze, and estimate 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.
See the case study →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 — causal structure from domain consensus, with corpus co-observation marked, and provenance in one interactive view.
Explore the graph →Build a bioelectrochemical reactor in the 3D lab and get live performance estimates with the interval the prior supports, an explicit note where none does, and mechanistic loss breakdowns.
Open the lab →Fix your operating conditions, sweep one variable, and watch estimates 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.
19,000+ MES papers in the corpus, 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, and about half keep a source snippet.
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}. Interval fields are filled where a calibrated interval exists.
Design a reactor and see the prediction, the empirical evidence behind it, and the mechanistic loss decomposition side by side.
Calibrated intervals
Literature measurements are normalised to SI, pooled into a hierarchical log-normal prior per system class, and wrapped in conformal intervals with measured coverage where calibration exists. 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, taken from domain consensus, or corroborated by co-observation in the corpus. Almost all rest on domain consensus, and none is established as causal.
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 estimated recovery
Six industrial archetypes + custom influent characterisation. Live estimates with a 95% interval where the prior supports one.
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
19,000+ papers · 214,682 measurements
Two-tier extraction (paper header + per-measurement conditions). Every row carries its DOI; about half carry a source snippet.
835 canonical parameters, FK-linked DAG
2,792 directed edges (snapshot of 22 Jul 2026): 2,721 rest on domain consensus, 11 are corroborated by corpus co-occurrence, none is established as causal. Multi-axis taxonomy (17 primary types × subtypes × combinations).
Per-class priors with measured interval coverage
Hierarchical priors (PyMC NUTS, v2): the 95% intervals cover 96.1% of 5,269 held-out values from 339 papers, scored 28 Oct 2026. That is interval coverage, not point accuracy.
Get started
Search the corpus, design a reactor, and read literature-based ranges that name their sources, their conditions and where no calibrated range exists.