You're about to commit to physics you can't verify yourself?
We verify it for you. In 48 hours. Deterministic physics rule-checking - not LLM pattern matching. Citable PDF report with a structured verdict on every technical claim.
// Who built this
Luc Durand
Founder & CTO
PhD Materials Science·CEA Saclay·Univ. Pierre & Marie Curie
Your investment thesis depends on the technology integrating with your existing platform. Generalist VC frameworks miss the materials-specific failure modes.
You're acquiring a company whose value depends on a materials breakthrough. The seller's claims need independent verification before the deal closes.
WE DELIVER
Deal-grade audit: every technical claim in the data room subjected to deterministic physics checks. Independent expert sign-off from senior PhD reviewer.
OUTCOME
Your deal team has a defensible technical position for the LOI, negotiation, and post-close integration risk.
Scoped engagement From €5,000 per target Expert sign-off included
The sample below is from Battery - our most calibrated domain (25 outcomes incl. Envia, Sakti3, Northvolt, Amprius, 85% verdict accuracy). The same engine runs across all 10 domains above.
INVESTMENT MEMOSIONIC ENERGY · MAY 2026
MatIntel · Battery Pre-Screen · May 2026 · Page 1
Sionic Energy
100% Silicon Anode Platform - Investment Memo
Verdict - assuming Series A, $10-15M raise
Pause pending 20Ah cycling data. Chemistry credible. Format translation unproven. All remaining risks fundable or ambiguous - no fatal risks.
~600 cyc at 20Ah vs 1,200 at 4-10Ah. $800K · 6 months to resolve.
Fundable
Supply chain (electrolyte)
Single-source thio-phosphate. $500K · 18 mo for 2nd supplier.
Ambiguous
Calendar aging
8%/yr at 35°C est. from HT data. $200K · 12-18 mo direct test.
FATAL RISKS: NONE DETECTED. No physics violations, no mechanism failures, no structurally uncompetitive cost positions identified.
matintel.ai/prescreen/batteryFor investment discussion only
INVESTMENT MEMOSIONIC ENERGY · PAGE 2
MatIntel · Battery Pre-Screen · May 2026 · Page 2
Resolving the ambiguous risk: calendar aging
Estimated 8%/yr at 35°C from HT data. A dedicated test ($200K · 12-18 mo) resolves the classification:
// IF POSITIVE
Aging ≤5%/yr at 35°C. Within industry norms (NMC: 3-5%/yr). Calendar aging → Resolved. Deal stronger.
// IF NEGATIVE
Aging >10%/yr or non-linear. Engineering can't fix it. Calendar aging → Fatal. Pass.
What the money proves
20Ah validation campaign
$800K
200 cells, 1,000+ cyc. 6 mo → Format translation Fund. → Resolved
2nd electrolyte supplier
$500K
Removes single-source. 18 mo → Supply chain Fund. → Resolved
First licensee integration
$700K
Process knowledge support. 12 mo → Licensing model proven.
Calendar aging test
$200K
Direct test, realistic duty cycle. 12-18 mo → Resolves only ambiguous risk.
$2.2M of the raise directly de-risks every open assumption. The rest funds operations, team, commercial development. You're not buying certainty - you're buying the right to fund the next proving steps.
matintel.ai/prescreen/batteryFor investment discussion only
INVESTMENT MEMOSIONIC ENERGY · PAGE 3
MatIntel · Battery Pre-Screen · May 2026 · Page 3
Deal progression - verdict changes by stage
Read left to right: each column shows the deal after one more risk resolves. Read top to bottom: same evidence → different verdicts by stage.
What just happened
Risks open
Seed
Series A
Series B
Today (pre-screen)
3 fund + 1 amb
Proceed
Pause
Too early
20Ah cycling ≥80%
2 fund + 1 amb
Proceed
Proceed
Pause
Calendar aging ≤5%/yr
2 fundable
Proceed
Proceed
Pause
2nd supplier qualified
1 fundable
Proceed
Proceed
Pause
Licensee yield >80%
None
Proceed
Proceed
Proceed
✕ Any answer fatal
≥1 fatal
Pass
Pass
Pass
Series A flips from Pause to Proceed after just one answer (20Ah cycling). Series B stays Pause until all four risks resolve. A single fatal answer makes the deal a Pass at every stage - money cannot solve physics failures.
matintel.ai/prescreen/batteryFor investment discussion only
TECHNICAL PRE-SCREENSIONIC ENERGY · PAGE 1
PhysicsCheck Pro · Sionic Energy · May 2026 · Page 1
20Ah cells: ~600 cycles vs 1,200 in small format. Format translation is the binding constraint. Temperature sensitivity (830→580 cycles at 25→45°C in 10Ah) compounds the risk.
Claim Breakdown - 5 testable assumptions
L0
SCC55 maintains <4% expansion at 20Ah, 1,000+ cyc
P=12
L1
Thio-phosphate SEI stable at 25°C and 45°C
P=8
L2
370 Wh/kg at 20Ah sustains >1,000 cycles
P=5
L3
Electrolyte additive supply scales at GWh volume
P=3
L4
Licensing partners drop-in at 100% SCC55 loading
P=2
Test order matters. Level 0 = test first. If expansion fails, all downstream collapses. Priority score = evaluation order (higher = test sooner).
matintel.ai/prescreen/batteryConfidential · Investment discussion only
TECHNICAL PRE-SCREENSIONIC ENERGY · PAGE 2
PhysicsCheck Pro · Sionic Energy · May 2026 · Page 2
Manufacturing & reliability flags
Fundable
Process-window sensitivity
Narrow window widens with investment. Not a physics barrier. 516 papers · 0 at 100%
Ambiguous
Calendar aging 45-60°C
8%/yr estimate from HT data. No direct test. 224 papers elevated-temp
N/A
Cell-to-cell variability, safety/abuse
Requires production data. Covered in Competitive set (€2,500).
Resolution Path - Prioritised Action Plan
P1
20Ah cycling: 1,000+ cycles at C/3, 1C, 25°C
2-4 weeks · Sionic or Element Materials
P1
Temperature cycling 25/45/0°C with EIS
2-4 weeks · Arrhenius analysis
P2
Electrolyte additive scale-up feasibility
€800-1,500 · Specialist call
P3
Cylindrical format (21700 or 4680) cycling
4-8 weeks · Sionic Energy
Specialist needed: electrolyte chemist with additive scale-up experience. Expansion solved (SCC55). Open question: thio-phosphate AEA at GWh cost.
matintel.ai/prescreen/batteryConfidential · Investment discussion only
1
Verdict by raise stage
Same evidence → different decisions for Seed, Series A, Series B. One company, four conditional verdicts. The visitor sees exactly when to act.
2
Four-category risk classification
RESOLVED · FUNDABLE · AMBIGUOUS · FATAL. The category determines the investment decision - not a numeric score. Each risk is auditable and citable.
3
Resolution path tied to capital
Every open risk → specific data point, cost-to-resolve, and timeline. You see exactly what the founder's raise actually buys.
4
Corpus-grounded, not generated
Every claim verified against the full corpus: 789K papers, 190K patents, 93K synthesis recipes across 10 materials domains. Battery is the most calibrated domain (25 retrospective outcomes, 85% verdict accuracy). Citable footnotes throughout.
Memo · p.1
Verdict + Risk table
5 risks classified
Investment memo · 1
Memo · p.2
Capital × Resolution
What the money proves
Investment memo · 2
Memo · p.3
Deal progression
Verdict × stage matrix
Investment memo · 3
Pre-screen · 1
5 testable assumptions
Verification report
Technical pre-screen · 1
Pre-screen · 2
Resolution plan
Who to ask · cost
Technical pre-screen · 2
// Get the full sample
Both documents · 5 pages · deal-grade PDF
Investment memo (3 pages) + technical pre-screen (2 pages) on Sionic Energy. For your reference library.
Free · No card · Instant download · Luc also gets the lead
// The architecture decision
LLMs read the documents. Physics rules render the verdict.
ChatGPT, Perplexity, and Claude can all write a plausible technical critique of your deal. None of them can tell you whether the claim violates physics - they have no notion of constraint. We use language models for what they're good at (extraction). The verdict comes from a deterministic rule engine. Always.
// ANALYST INPUT
"Our anode delivers 370 Wh/kg at 1,200 cycles in 20Ah cells. Is this credible for Series A?"
↓
↓
// LLM RESPONSEGPT-class · Claude · Perplexity
"The claim of 370 Wh/kg with 1,200 cycles is ambitious but credible for silicon anode technology. Recent advances in silicon-carbon composites have shown promising results in laboratory settings. The 20Ah format would require further validation, but the underlying chemistry appears sound based on published research…"
✕ No verdict
✕ No citations
✕ Different output every time you ask
✕ Confidently plausible - wrong as often as right
// PHYSICSCHECK ENGINEmatintel.ai · deterministic
VERDICTFUNDABLE - pending 20Ah data
Energy density (370 Wh/kg)
PASS
821 papers · 148 confirm
Cycle life (1,200)
WARN
Small format only
Format translation (20Ah)
GAP
0 papers ≥1,000 cyc · 100% Si
Resolution path: $800K · 6 months · 20Ah validation campaign.
If 20Ah cycling ≥80% of baseline → PROCEED to Full DD.
✓ Calibrated 85% on 25 battery outcomes (1st of 10 domains)
LLMGPT · Claude · Perplexity
PhysicsCheckMatIntel engine
Architecture
Pattern matching over training data
Deterministic rule engine
Verdict source
The model's prediction
Composition of 12 physics constraints
Reproducibility
Different output each run
Identical inputs → identical verdict
Failure mode
Confident hallucination
Returns FATAL or refuses to verdict
Co-developed by Joachim Roesch (PhD Chemistry, Univ. Freiburg, 23 years VP Innovation at BASF, GLG) and Luc Durand (PhD Materials Science, CEA Saclay / Univ. Pierre & Marie Curie). The methodology is the moat. The LLM is the typist.
Battery is the first of ten domains calibrated. Back-tests include Envia · Sakti3 · Northvolt · Amprius - 85% verdict accuracy on the battery set.
// Not ready to commit?
A free preliminary physics score. 48 hours. Personally reviewed.
One claim. One email. You receive back: a single physics-checked verdict (RESOLVED / FUNDABLE / AMBIGUOUS / FATAL), one paragraph of reasoning grounded in our corpus, and one citation. No card, no call. Delivered in 48 hours because real verification takes real work.
✓ 48h delivery✓ Luc reviews every send personally✓ 1 per company · max 10/week
Informational purposes only. Not investment advice. Luc reviews each submission personally before sending.
Every technical claim decomposed into testable assumptions, verified against 789K papers and 190K patents, checked through 12 deterministic physics rules. You receive a structured PDF - not a chat transcript, not a score.
STEP 01
CLAIM
You submit the claim
Drop a 1-page summary or pitch deck. Company name + the core technology claim is enough.
Time:2 min
Input:PDF · text
STEP 02
PARSE
We extract testable assumptions
Claim decomposed into 3-7 falsifiable assumptions, ranked by priority (Level 0 = weakest link).
Time:4 hrs
Step:Internal
STEP 03
VERIFY
We check each against the corpus
Every assumption verified against 789K papers, 190K patents, 93K synthesis recipes. Citations attached.
Time:12 hrs
Output:Evidence
STEP 04
RULES
We run 12 deterministic physics rules
Band gap, cycle life, expansion, energy density, areal capacity, more. Each returns PASS / WARN / FATAL.
// What "verified against 789K papers" actually looks like
Every parameter, benchmarked against literature.
The corpus isn't a search engine - it's a distribution. When a claim arrives, each parameter gets physically positioned against the literature distribution for its material class. You see exactly where the claim sits, with citation counts. Below: a real example from the Sionic Energy pre-screen.
Sionic's <4% expansion at 100% Si is well below the typical 15-25% for unmitigated silicon. The SCC55 scaffold contains the volume change internally - mechanism is established.
Cycle life · small format (4-10 Ah)cycles to 80% capacity
Resolved
1,200
200600p251,000target1,500p902,500
154 papers report >1,000 cycles at small format · 32 with CE >90%
1,200 cycles in 4-10 Ah cells is within established literature for thio-phosphate AEA SEI chemistry. Format-translation question is the open one (see below).
Cycle life · 20 Ah formatcycles to 80% capacity
Fundable
600
200500p251,000target1,5002,000
0 papers report 100% Si at 20 Ah ≥1,000 cycles · format translation unproven
~600 cycles at 20 Ah is 50% below the small-format baseline. Trend is positive but the gap hasn't closed. This is exactly what Series A money should fund: $800K · 6-month validation campaign.
Si loading at 20 Ah format% by mass
Fundable
100%
5%10%typical20%aggressive50%max published
beyond literature
516 papers reference Si loading · 0 demonstrate 100% loading at 20 Ah scale
100% Si is the headline claim - and it sits beyond every published precedent at the 20 Ah format. R10 extraordinary-claim rule fires as WARN (not FATAL) because the mechanism is plausible. Resolution: $700K licensee integration support over 12 months.
Five parameters. Five verdicts. Three Resolved (chemistry is established). Two Fundable (specific data points missing). Zero FATAL. This is exactly what the engine returns - position against literature, color-coded by rule firing, citation count attached. Not a score. Not a recommendation. The structured truth about what's known and what isn't.
// What you don't have to do
Five things you never touch.
Sophisticated due diligence usually means three weeks of analyst work. Here it's our work, not yours.
✕
You don't write the corpus query
We translate the claim into the right search vocabulary - including jargon synonyms across patent classifications and academic disciplines.
✕
You don't validate citations
Every cited paper is checked for relevance, not just hit count. If a paper appears in your report, we've read at least the abstract and methods section.
✕
You don't translate physics jargon
The report is written for an investment audience, not a journal review board. Every technical claim has a plain-English implication.
✕
You don't second-guess the verdict
Every verdict is auditable: 12 rules, each returns PASS/WARN/FATAL, full firing log available on request. The verdict isn't a black-box score.
✕
You don't pay before delivery
Zero card-on-file. Invoice comes after the report is in your inbox. If we miss the 48h SLA, you don't pay at all.
// Timing & SLA
48 hours, end to end. No exceptions.
From the moment your claim arrives in our inbox to the moment the PDF arrives in yours.
T+0Claim received
T+4hParsed
T+16hVerified
T+24hCheckpoint email
T+48hPDF delivered
If we miss 48 hours, you don't pay. The clock starts when your submission lands in our inbox and stops when the PDF is sent back. We've never missed it. The guarantee is in writing on every invoice.
// Honest scope
What we do - and what we deliberately don't.
A focused tool beats a generalist tool. We're the focused tool.
✓ In scope
Physics feasibility - does the claim violate any physical law or established envelope?
Scale-up risk - what changes when you move from lab to GWh / 20Ah / commercial cell?
Corpus evidence - what does the literature actually say? With citations.
Resolution path - for each open risk, what data point resolves it, at what cost.
Per-stage verdict - same evidence, different verdicts for Seed vs Series A vs Series B.
✕ Out of scope
Valuation - we don't tell you what the company is worth.
Team assessment - we don't grade the founders or background-check the CTO.
Market timing - whether now is the right moment to invest.
Legal / IP review - patent filings are checked for evidence, not freedom-to-operate.
Financial due diligence - revenue projections, unit economics, cash burn.
We do exactly one thing extremely well: physics verification of technical claims, against a 789K-paper corpus, in 48 hours, with a citable PDF as the deliverable. Everything else, you have other tools for.
// Pricing
Two tracks. One ladder.
Same engine, two pricing structures - shaped to how VCs and founders actually buy due diligence.
// For VCs
VC track
Per-deal pricing, scoped per engagement. Start with a single pre-screen; expand to a competitive set when a partner needs to see the field, not just the target.
Below the typical CFO approval threshold. A CTO can authorise it. Useful before a raise: catch the physics objections an investor will ask before they ask them.
Above the ladder: Free preliminary score · 48h · Luc reviews personally
|Below the ladder: Annual subscription €5K-7.5K/yr (10 pre-screens) - offered after 3-5 delivered reports
|Outside the ladder: Corporate VC strategic deep-dive from €1,500 · M&A / Procurement from €5,000
// Common questions
Asked often.
No. LLMs are used for one thing only: extracting structured information from unstructured documents (your pitch deck, papers). The verdict itself comes from a deterministic 12-rule physics engine. Same input always produces the same verdict - which no LLM can guarantee. See the Methodology page for the architectural details.
Ten advanced materials domains: Battery, Semiconductors, Hydrogen, Solar, Ceramics, Composites, Thermal Barrier, Superconductors, Catalysis, Additive Manufacturing. Battery is the most calibrated (25 outcomes back-tested at 85% accuracy). The same engine runs across all ten.
Yes. The full Sionic Energy report - both documents, 5 pages - is available as a free download on the landing page (Sample reports section). It's the exact format you'd receive for your own deal.
Tell us. We've expanded the corpus to new domains before (each takes ~2-3 weeks of ingestion and calibration). If you have 3+ deals in the same adjacent domain, we'll prioritise building it out.
Luc Durand (PhD Materials Science, CEA Saclay · Univ. Pierre & Marie Curie) builds and operates the engine. Joachim Roesch (PhD Chemistry, Univ. Freiburg · 23 years VP Innovation at BASF · GLG) validates every verdict before sign-off. Independent blind reviewer: Dr. Dominic Bresser (KIT / HIU) for battery deals.
Ready to run a deal through this?
Submit your claim. Pay €750 only after the PDF is in your inbox.
LLMs read the documents. Physics rules render the verdict.
The architectural commitment that makes our verdicts reproducible. Every claim is decomposed by a language model, then evaluated by a deterministic engine of 12 physics rules that runs across all 10 materials domains. The battery domain is the most empirically calibrated to date: 85% verdict accuracy on 25 retrospective outcomes. Other domain calibrations in progress.
// ANALYST INPUT
"Our anode delivers 370 Wh/kg at 1,200 cycles in 20Ah cells. Is this credible for Series A?"
↓
↓
// LLM RESPONSEGPT-class · Claude · Perplexity
"The claim of 370 Wh/kg with 1,200 cycles is ambitious but credible for silicon anode technology. Recent advances in silicon-carbon composites have shown promising results in laboratory settings. The 20Ah format would require further validation, but the underlying chemistry appears sound..."
✕ No verdict
✕ No citations
✕ Different output every time you ask
✕ Confidently plausible - wrong as often as right
// PHYSICSCHECK ENGINEmatintel.ai · deterministic
VERDICTFUNDABLE - pending 20Ah data
Energy density (370 Wh/kg)
PASS
821 papers · 148 confirm
Cycle life (1,200)
WARN
Small format only
Format translation (20Ah)
GAP
0 papers · 100% Si at 20Ah
Resolution path: $800K · 6 months · 20Ah validation campaign.
If 20Ah cycling ≥80% of baseline → PROCEED to Full DD.
Language models are extraordinary at reading unstructured documents. They are not extraordinary at physics. Our architecture uses them where they excel - extracting and structuring claims - and prohibits them from the part that needs to be reproducible: the verdict.
// LLM ZONEExtraction LayerLLMs allowed
1
PARSE
370 Wh/kg · 1,200 cyc
Read pitch deck. Identify every quantitative technical claim.
→
2
DECOMPOSE
CLAIM
A1A2A3A4
Break claim into 3-7 falsifiable, testable assumptions.
→
3
RANK
L0weakest
L1
L2
Identify the weakest link (L0). If L0 fails, downstream collapses.
↓
structured claim handed to physics engine
// PHYSICS ZONEVerdict EngineNo model in loop
4
BENCHMARK
p10medianp90
claim vs corpus distribution
Every parameter positioned against the literature distribution. Citation count attached.
→
5
VERIFY
R01
R02
R03
R04
R05
R06
R07
R08
R09
R10
R11
R12
12 deterministic rules fired in parallel. Each returns PASS / WARN / FATAL independently.
→
6
VERDICT
ResolvedFundableAmbiguousFatal
Deterministic composition of all 12 outputs. Same inputs, same verdict, every time.
LLM zone: extraction only. Reading documents, structuring claims, ranking by priority - reproducible enough at this layer.
Physics zone: no LLM in the loop. Benchmarking, rule firing, verdict composition - fully deterministic, fully citable, fully auditable.
// Comparison
LLM vs PhysicsCheck - across seven dimensions.
The architectural difference is not subtle. Every dimension matters when a partner is staking €15K of consultant fees or a €10M investment on the answer.
LLMGPT · Claude · Perplexity
PhysicsCheckMatIntel engine
Architecture
Pattern matching over training data
Deterministic rule engine
Verdict source
The model's prediction
Composition of 12 physics constraints
Reproducibility
Different output each run
Identical inputs → identical verdict
Failure mode
Confident hallucination
Returns FATAL or refuses to verdict
Provenance
No citations
Every claim → corpus reference (papers + patents)
Calibration
None disclosed
85% verdict accuracy on 25 historical battery outcomes
Auditability
Black box
12 rules, fully open specification - downloadable PDF
// The 12 deterministic physics rules
Each rule, in depth.
Each rule independently returns PASS, WARN, or FATAL. The verdict is the deterministic composition of all 12 outputs - no model in the loop. Below, each rule with its physical envelope, failure semantics, and a worked example.
R01
Active
band_gap_bounds
Validates semiconductor band gap claims against the universal upper limit. Eg < 14 eV for any solid-state material. Wider claims are physically impossible.
Failure semantics
PASS: Eg within ±20% of literature range for the material class. WARN: ±20-50% drift. FATAL: outside thermodynamic bound (≥14 eV).
Worked example
Claim: "Wide-gap perovskite at 4.2 eV, suitable for tandem solar." Literature range for halide perovskites: 1.5-3.2 eV. WARN: outside class range, requires composition justification.
R02
Active
formation_energy
Rejects thermodynamically impossible compounds. |Ef| ≤ 5 eV/atom for synthesisable inorganic phases. Beyond this bound, the proposed compound cannot exist stably.
Auto-recovers density unit ambiguities (g/cm³ vs kg/m³ vs mg/mL). Refuses to verdict on bare numbers without unit. Prevents the most common LLM hallucination class - unit-swapped values.
Failure semantics
PASS: unit explicit + within class. WARN: unit ambiguous but recoverable. FATAL: no unit + values inconsistent across mentions.
Worked example
Claim: "Lithium metal density 0.534" (no unit). g/cm³ matches lit (0.534). kg/m³ would be vacuum-grade. WARN: unit recovered, but flagged for human confirmation.
R05
Active
cycle_life_consistency
Cycle count × C-rate × DoD must satisfy the degradation envelope for the cell chemistry. Catches the "1,000 cycles at 5C, 100% DoD" combinations that aren't physically achievable.
Failure semantics
PASS: cycle × rate × DoD within published envelope. WARN: at edge. FATAL: combination physically incompatible with the chemistry.
Worked example
Claim: "10,000 cycles at 4C, 100% DoD in NMC811." Lithium plating + cathode fade make this combination FATAL. No published precedent.
R06
Active
energy_density_envelope
Wh/kg vs Wh/L mutual plausibility check per chemistry class. Detects the cases where one metric is realistic but its volumetric/gravimetric counterpart is impossible.
Failure semantics
PASS: both gravimetric and volumetric within class. WARN: one at edge, other within. FATAL: one realistic, the other physically impossible given density.
Worked example
Claim: "400 Wh/kg AND 1,200 Wh/L Li-ion." Class density ~2.0 g/cm³ implies ~800 Wh/L max. FATAL: volumetric is impossible at stated gravimetric.
R07
Active
temp_coefficient
Temperature-dependent property claims (α(T), conductivity drift, capacity fade) checked against material-class Arrhenius envelopes. Sign and magnitude both validated.
Failure semantics
PASS: α(T) sign + magnitude within class. WARN: sign correct, magnitude at edge. FATAL: wrong sign or order-of-magnitude error.
Worked example
Claim: "Negative temperature coefficient for ionic conductivity in solid electrolyte." Standard solid electrolytes show positive α(T) via Arrhenius. FATAL unless polaron or phase-transition mechanism is documented.
R08
Active
coulombic_efficiency
Initial CE (ICE) and steady-state CE bounded by thermodynamic limits per cell chemistry. Catches the "99.99% CE" hyperbole that no real cell sustains.
Failure semantics
PASS: CE within class envelope. WARN: ICE matches but steady-state at edge. FATAL: CE > thermodynamic ceiling for chemistry.
Worked example
Claim: "ICE 95% at first cycle, 100% Si anode." Si literature ICE: 70-85% typical, 90% best-published. WARN: at extreme edge; requires scaffold/pre-lithiation evidence.
R09
Active
areal_capacity
Areal capacity (mAh/cm²) within literature-reported envelopes for the active material and cell format. Catches the "4 mAh/cm² thin-film" misclassifications.
Failure semantics
PASS: areal × thickness × density consistent. WARN: at upper edge. FATAL: requires loading that exceeds physical packing density.
Worked example
Claim: "10 mAh/cm² Si anode, 50 μm coating." Theoretical max at 100% Si, 50 μm, 80% packing: ~12 mAh/cm². PASS but at edge - validate thickness.
R10
Active
extraordinary_floor
The R10 extraordinary-claim rule. Any claim ≥ 3× the published literature maximum returns FATAL automatically, regardless of other rules. Carl Sagan's principle in code form.
Failure semantics
PASS: ≤ literature max. WARN: 1-3× literature max. FATAL: ≥ 3× literature max (extraordinary-claim threshold).
Worked example
Claim: "2,500 Wh/kg solid-state Li-air." Published Li-air max: 800-900 Wh/kg. Ratio: 2.8×. WARN at threshold; FATAL if cited as commercial-ready.
R11
Active
activation_energy
Activation energy (Ea) for transport / reaction processes validated against material-class Arrhenius ranges. Detects rate-limited claims that violate transport physics.
Failure semantics
PASS: Ea within published class range. WARN: at edge. FATAL: Ea too low to be thermally consistent with the rate claimed.
Worked example
Claim: "Solid electrolyte conductivity at -40°C = 1 mS/cm." Implies Ea ≈ 0.05 eV - too low for solid Li+ transport (typical 0.3-0.5 eV). FATAL.
R12
Active
crystal_symmetry
Space group + lattice parameter consistency. Cross-validated against Materials Project, AFLOW, NOMAD. Detects mis-specified crystal structures that don't match the stated chemistry.
Failure semantics
PASS: space group matches stoichiometry in MP/AFLOW. WARN: novel phase, not in databases. FATAL: stoichiometry incompatible with reported space group.
Worked example
Claim: "LiCoO2 in P63/mmc hexagonal." LiCoO2 is R-3m (layered rhombohedral). P63/mmc is wrong space group. FATAL: misclassification.
The engine, retrospectively run against public deals.
The methodology runs across ten materials domains: battery, semiconductors, hydrogen, solar, ceramics, composites, thermal barrier, superconductors, catalysis, additive manufacturing. The battery domain is currently the most empirically calibrated - shown below. The same architecture, with domain-specific corpus and rule tuning, applies to all ten.
Twenty-five battery companies whose technology claims were publicly known and whose ultimate fate is now public record. We ran the engine against their pitch-stage claims (date-fenced inputs, blind verdict generation) and compared the prediction to the actual outcome. Eight selected examples below - the full table is in the back-test report PDF.
→ swipe to see outcomes & match
Company
Year
Claim summary
MatIntel verdict
Actual outcome
Match
Envia Systems
2012
400 Wh/kg Li-ion at automotive scale
FATAL
Company failed by 2014; partnerships terminated
✓
Sakti3
2014
Solid-state Li-metal at >1,000 Wh/kg
AMBIGUOUS
Acquired Dyson 2015 for $90M; tech never commercialized
✓
A123 Systems
2009
LFP at commercial automotive scale
FUNDABLE
Chapter 11 in 2012; acquired by Wanxiang
✕
Northvolt
2017
GWh-scale European Li-ion gigafactory
FUNDABLE
Operating at scale; multiple gigafactories online
✓
Amprius Tech.
2010
Silicon nanowire anode, >400 Wh/kg
FUNDABLE
Public 2022; shipping at aerospace and EV scale
✓
QuantumScape
2020
Solid-state Li-metal, ceramic separator
AMBIGUOUS
Pre-commercial; cycling milestones delayed
✓
Sila Nanotech.
2018
Si-dominant anode, drop-in commercial
FUNDABLE
Shipping to Mercedes-Benz, BMW; qualified at scale
✓
Solid Power
2021
Sulfide solid electrolyte at automotive scale
FUNDABLE
Operating with timeline delays; progressing
✓
Aggregate verdict accuracy across all 25 battery outcomes:
21 of 25 verdicts matched actual outcome at the originally-predicted classification level. Misses include 1 false-fundable (A123 above) and 3 false-cautions. This figure is battery-domain only - companion calibration sets for the other nine domains are in development.
85%
Back-test protocol: date-fenced inputs (only papers and patents available before the pitch date were used), blind verdict generation (no foreknowledge of outcomes during engine run), manual review by both methodology authors. Full audit trail and per-outcome reasoning in the downloadable battery calibration report below.
// Authorship
Who designed and validates the engine.
The methodology is co-developed by two senior materials scientists with complementary backgrounds: deep academic foundations and decades of industrial practice.
Luc Durand
Founder & CTO
PhD Materials Science, CEA Saclay · Univ. Pierre & Marie Curie (now Sorbonne University). Builds and operates the engine. Background in electrochemistry, thin-film characterisation, and computational materials physics. Responsible for the corpus pipeline, the 12-rule engine, and the calibration protocol.
PhD Chemistry, Univ. Freiburg · 23 years VP Innovation at BASF · GLG. Validates every verdict before sign-off. Brings the industrial reality check: what a chemistry claim looks like at lab scale vs pilot vs GWh. Specialist on cathode materials, electrolyte additives, and scale-up risk.
The methodology in detail (12-rule formal specification, cross-domain) and the battery-domain back-test calibration report (25 outcomes with full audit trail). Both PDFs, both free.
// Specification
Methodology Spec v2
The 12-rule formal specification. Pseudocode, PASS/WARN/FATAL semantics per rule, composition logic for the final verdict. For technical reviewers.
PDF · 18 pages · v2.5 (May 2026)
// Empirical proof · battery
Battery Calibration Back-Test
Battery-domain back-test of the engine. All 25 publicly-known battery outcomes with full per-deal audit trail: date-fenced inputs, blind verdict generation, comparison to actual outcome. The empirical evidence behind the 85% headline for this domain. Companion reports for the other nine domains in preparation.
PDF · 32 pages · 25 battery outcomes · May 2026
Ready to run a deal through this methodology?
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The empirical foundation. 789,412 papers. 10 materials domains.
The MatIntel corpus spans all ten materials domains the engine covers - battery, semiconductors, hydrogen, solar, ceramics, composites, thermal barrier, superconductors, catalysis, additive manufacturing. This page details the battery slice: what's behind every battery verdict the engine returns.
// Full corpus · across all 10 materials domains
789,412
scientific papers
190,247
patent filings
93,184
synthesis recipes
574,068
extracted parameters
// Battery domain · this page's focusThe battery slice~18% of papers · ~25% of patents · ~20% of recipes
// Battery papers
142,847
peer-reviewed + preprint
Across all battery sub-areas. Indexed for full-text retrieval, parameter extraction, and citation graph traversal.
18% of total corpus · sync 03 Jun 2026
// Battery patents
47,213
9 patent offices
Filed, granted, and pending. USPTO, EPO, JPO, WIPO, KIPO, CNIPA, IPI, INPI, DPMA. Linked to assignee + inventor graphs.
25% of total corpus · sync 02 Jun 2026
// Battery recipes
18,469
structured procedures
Reagents, conditions, yields, characterisation. Sourced from CEDER, DiffSyn, supplementary materials, and patent claims.
20% of total corpus · sync 01 Jun 2026
// Battery outcomes
25
retrospectively tested
Publicly known battery companies with date-fenced pitch claims and known outcomes. Engine retro-run; verdicts compared blind.
First domain calibrated · match rate 85%
// Sub-domain coverage · battery
Where the 142,847 papers live.
Battery research isn't one field - it's ten. Coverage varies by sub-area: Li-ion is mature with deep literature, multi-valent ion chemistries are sparser. The engine knows where the corpus is thick and where it's thin - and surfaces that in every verdict.
Li-ion (NMC, NCA, LFP)
cathode + cell-level chemistry
papers48,212
Silicon & Si-composite anodes
expansion, SEI, scaffold designs
papers21,304
Solid-state batteries
sulfide, oxide, polymer electrolytes
papers18,116
Solid electrolytes (Li transport)
conductivity, interface stability
papers14,728
Sodium-ion (Na-ion)
low-cost grid storage chemistry
papers12,047
Sulfur cathodes (Li-S, Na-S)
polysulfide shuttle, high-energy
papers9,142
Polymer & gel electrolytes
flexible cells, safety
papers8,461
Flow batteries (V, Zn, Fe)
long-duration grid storage
papers6,288
Li-metal anode (lithium plating)
dendrite suppression, SEI engineering
papers5,134
Multi-valent ion (Mg, Ca, Al, Zn)
post-Li chemistries
papers4,217
// Calibration · 25 retrospective outcomes
Every prediction. Every outcome. No cherry-picking.
Twenty-five battery companies whose pitch-stage claims were publicly verifiable and whose ultimate fate is now public record. The engine was retro-run with date-fenced inputs (only papers and patents available before the pitch date), verdicts generated blind (no foreknowledge of outcomes), then compared. Match rate: 21 of 25. The four misses are listed below the table - because hiding them would be the easiest way to inflate a number.
→ swipe to see outcomes & match
Company
Year
Claim summary
Verdict
Actual outcome
Match
Envia Systems
2012
400 Wh/kg Li-ion at automotive scale
FATAL
Company failed 2014; partnerships terminated
✓
Sakti3
2014
Solid-state Li-metal at >1,000 Wh/kg
AMBIGUOUS
Acquired Dyson $90M; tech never commercialised
✓
A123 Systems
2009
LFP at commercial automotive scale
FUNDABLE
Chapter 11 in 2012; acquired by Wanxiang
✕
Northvolt
2017
GWh-scale European Li-ion gigafactory
FUNDABLE
Operating; multiple gigafactories online
✓
Amprius Tech.
2010
Silicon nanowire anode, >400 Wh/kg
FUNDABLE
Public via SPAC 2022; shipping aerospace + EV
✓
QuantumScape
2020
Solid-state Li-metal, ceramic separator
AMBIGUOUS
Pre-commercial; cycling milestones delayed
✓
Sila Nanotech.
2018
Si-dominant anode, drop-in commercial
FUNDABLE
Shipping to Mercedes-Benz, BMW; qualified at scale
✓
Solid Power
2021
Sulfide solid electrolyte at automotive scale
FUNDABLE
Operating with timeline delays; progressing
✓
Boston-Power
2011
High-energy Li-ion for EV + portable
FUNDABLE
Operating (China-focused); acquired by GSR Capital
✓
Group14 Tech.
2020
SCC55 Si-C composite anode at scale
FUNDABLE
Scaling production; multiple OEM partnerships
✓
Enovix
2018
3D Si-cell architecture, drop-in form factor
AMBIGUOUS
Operating, public; shipping at scale
✕
ProLogium
2017
Ceramic solid-state Li at automotive scale
FUNDABLE
Scaling production; €5B factory financed
✓
NanoGraf
2019
Si-rich anode, 18650 +25% energy density
FUNDABLE
Operating; defense + consumer electronics
✓
Lyten
2022
3D graphene Li-S, decoupled from rare metals
FUNDABLE
Scaling; multiple defense contracts
✓
Form Energy
2020
Iron-air 100-hour grid storage
AMBIGUOUS
Operating; West Virginia factory online
✕
Eos Energy
2018
Zinc-bromide aqueous flow at grid scale
FUNDABLE
Operating; manufacturing in PA, multiple deployments
Mass-market Li-ion + sodium-ion at automotive scale
FUNDABLE
World's largest battery maker; CN-listed
✓
Influit Energy
2020
Nano-electrofuel for EV + grid
AMBIGUOUS
Active scaling; defense deployments confirmed
✕
Aggregate accuracy: 21 of 25 verdicts matched actual outcome at the classification level (RESOLVED / FUNDABLE / AMBIGUOUS / FATAL). Misses: 1 false-fundable (A123) + 3 false-cautions (Enovix, Form Energy, Influit Energy) - the engine was too conservative on three companies that ended up succeeding.
85%
// 1 false-fundable
A123 Systems (2009): The engine predicted FUNDABLE based on LFP chemistry maturity. The actual failure was driven by manufacturing yield + automotive customer churn - both outside the engine's scope (physics is fine, operations weren't).
// 3 false-cautions
Enovix, Form Energy, Influit: The engine flagged AMBIGUOUS where outcomes ended up positive. Pattern: novel cell architectures with limited literature support at pitch time. Now part of the rule-tuning feedback loop.
// Sources & provenance
Where every paper, patent, and recipe comes from.
No black-box ingestion. Every source is named, every update cycle is documented, every paper is traceable to its origin.
// Academic literature
Peer-reviewed papers + preprints
OpenAlexarXivChemRxivScopusWeb of ScienceCOREPubMedCrossref
142,847 papers · full-text where available · DOI-linked · daily delta sync
Cycling data · degradation studies · commercial format characterisation · quarterly
Papers syncDaily 02:00 UTC
Patents syncWeekly Mon 04:00
Materials DBMonthly 1st
Re-indexingNightly 03:00 UTC
// Honest limits
What the corpus doesn't see.
A verdict is only as good as the evidence behind it. Below is a frank list of what the engine cannot verify from the corpus - the questions that need a domain expert call, an NDA-protected datasheet, or production data to resolve.
// Outside the corpus
Internal R&D and unpublished data. We see only what's been published, patented, or disclosed in regulatory filings. Pre-publication results are invisible.
Manufacturing yield and economics. Cost-per-kWh, scrap rates, capex-per-GWh: these are operational data, not physics. The engine will flag where they matter and recommend Full DD.
OEM qualification reports and customer-specific data. Tesla's qualification of Sila, BMW's of Solid Power - these are NDA-bound and absent from public sources.
Recent (less than 30 days) publications still indexing. New papers take 1-4 weeks to flow through OpenAlex / Crossref into our nightly indexer. Cutting-edge results may be temporarily missed.
Cell-to-cell variability and safety / abuse-tolerance data. These require physical testing that doesn't reach published literature for commercial cells.
Patent quality and enforceability. We see filings; we don't predict litigation outcomes. Freedom-to-operate analyses need IP counsel.
// Sample queries
What the engine answers, on demand.
A small sample of the questions the corpus + engine can resolve, with corpus-grounded responses. These are the kinds of inputs that feed every pre-screen verdict.
How many papers report >1,000 cycles at 100% Si anode in cells ≥20 Ah format?
↓
0 papers meet all three thresholds simultaneously. 154 papers report >1,000 cycles at small format (4-10 Ah). 32 papers additionally report ICE >90%. The format-translation gap is a documented bottleneck across the literature.
What is the literature distribution of energy density for solid-state Li-metal cells at commercial scale?
↓
18,116 papers on solid-state batteries. Energy density envelope at >10 Ah scale: p10 = 200 Wh/kg, median = 280 Wh/kg, p90 = 360 Wh/kg. Claims above 400 Wh/kg fire the R10 extraordinary-claim rule.
Which companies have published thio-phosphate AEA electrolyte patents in the last 5 years?
↓
57 patents across 12 assignees. Sionic Energy (JP-2025016696-A confirmed); also assignees include Solid Power, QuantumScape, ProLogium, Samsung SDI. Inventor graph reveals 3 cross-assignee mobility patterns.
Ready to run a battery deal through this corpus?
Submit your claim. Engine + 12 deterministic rules produce a verdict in 48-72 hours. Pay only after the PDF is delivered.
Audits of trending materials, methodology analysis, and notes from the work of verifying technical claims for investors, founders, and operators. New posts when something is worth writing about - not on a schedule.
Why a leading LLM gave us two contradictory answers about the exact same battery claim - and what that means for AI-assisted due diligence.
0.995 to the power of 2,000 equals 0.0044%.
After two thousand cycles, a battery with 99.5% Coulombic efficiency retains 0.004% of its capacity. The cell is dead - effectively, around cycle 45.
Yet last week I asked a leading LLM the same claim two different ways. One answer caught the physics. The other was a confident hallucination that, read by a VC at 11pm before IC, would have led straight to a bad investment.
The math no LLM seems to run by default
Coulombic efficiency (CE) compounds. If a cell loses 0.5% of its usable charge every cycle, it doesn't lose 0.5% × 2,000 over the system's life. It loses 0.5% of whatever capacity remains at the start of each cycle. The arithmetic is brutal:
Cycle
Capacity retained
45
80.0% ← industry end-of-life threshold crossed
100
60.6%
200
36.7%
500
8.2%
1,000
0.7%
2,000
0.004%
A 99.5% CE cell is effectively dead at cycle 45. The pitch said 2,000. Anyone who scaled that claim through a basic compounding calculation would have stopped reading.
Two prompts, one LLM, two opposite answers
I asked a major frontier LLM about this claim in two ways. The cell, the numbers, the chemistry - all identical. Only the framing changed.
// PROMPT 1 - phrased as a marketing-style statement
"What do you think about this cathode: it achieves competitive energy density with excellent cycling stability. 99.5% CE over 2,000 cycles demonstrates strong capacity retention."
× LLM answer
"This represents highly promising performance for a next-generation battery system, particularly if this cathode is based on high-voltage spinel, solid-state, or advanced lithium-metal chemistries. A 99.5% Coulombic Efficiency over 2,000 cycles indicates minimal parasitic side reactions and excellent reversibility..."
The LLM glossed over the math. It read the framing as "this is good," and confirmed.
// PROMPT 2 - phrased as a math question
"If a battery has 99.5% Coulombic efficiency over 2,000 cycles: what does that mean, and is it a long-lasting battery system?"
✓ LLM answer
"A Coulombic efficiency of 99.5% over 2,000 cycles means the battery loses 0.5% of its usable charge on every single cycle, resulting in an unusable, rapidly degrading system long before reaching cycle 2,000. Despite sounding high, a 99.5% CE is not a long-lasting battery system for deep-cycling applications."
The math was always there. The LLM only ran it when the prompt explicitly demanded it.
// Capacity retention by Coulombic efficiency · 2,000 cycles
Two cells. Same starting capacity. Only 0.49 percentage points of CE separating them. The red cell crosses the industry end-of-life threshold (80% retention) at cycle 45 and is effectively a brick by cycle 200. The green cell is what a real production Li-ion looks like - and it required 0.49 points of additional CE to get there.
Why this matters for due diligence
Pitch decks are written to sound impressive. A "99.5% CE over 2,000 cycles" claim, placed next to "excellent cycling stability" without context, reads as a strength to anyone - human or LLM - who isn't running the math from first principles.
A deterministic physics engine doesn't have this failure mode. The CE compound-decay rule is one of twelve constraints that fires on every claim, every time, regardless of how the claim is phrased. The math doesn't care about prompt framing.
Three takeaways for investors:
Test your tools. Ask the same physics question two different ways. If you get two different answers, the tool isn't doing physics - it's doing pattern matching on language.
Reframe everything. Before trusting any verdict on a technical claim, rewrite the claim in mathematical terms. If the verdict changes, the verdict was unreliable.
The math is the moat. Anything that can be spin-doctored by a marketing copywriter shouldn't be the foundation of a 5-million-euro investment decision.
This is precisely why MatIntel uses LLMs only for extraction (reading the documents) and a 12-rule deterministic engine for the verdict. Same input, same verdict, every time - whether the founder writes "99.5% CE over 2,000 cycles" or "the cell loses 0.5% per cycle, compounded." The R6 cycle-life rule fires either way.
Linden's Handbook of Batteries (4th ed.), §22.5 - Coulombic efficiency and capacity fade modeling
IEEE Trans. on Energy Conversion, 2018: "Coulombic efficiency and battery life modeling"
NREL CALCE: open Li-ion cycling datasets (publicly available baseline for 99.95%+ CE production cells)
Live LLM responses captured 04 Jun 2026 (frontier model, anonymized per provider T&Cs)
04 Jun 2026Field notes5 min read
Six readers. One question. Every Monday.
After ~25 pre-screens, the same underlying question arrives in six different forms. Here's what they're actually asking - and what I've learned about each one.
Every Monday morning I open the same inbox and see the same six readers asking the same question.
They look unrelated. A VC partner with an IC on Thursday. A hard-tech founder with a Series A deck. A procurement officer evaluating a battery supplier. They all phrase their question differently, in different jargon, on different deadlines.
Underneath the surface, they're all asking the same thing.
The question, in six different costumes
// Six readers · six framings · one shared question
// 01 · VC PARTNER
Pre-investment-committee
"Will this physics hold up when our LP-funded advisor reviews the deal next week?"
// 02 · HARD-TECH FOUNDER
Pre-Series-A raise
"What will an investor's physicist friend find when they read my deck on Saturday?"
// 03 · CORPORATE VC
Strategic fit + technical fit
"Does this fit our parent's roadmap AND does the chemistry actually work?"
// 04 · M&A ADVISOR
Target identification
"Are we acquiring real defensible tech, or a compelling story dressed in patents?"
// 05 · PROCUREMENT
Strategic sourcing & OEM qual
"This supplier's spec sheet is impressive. Will any of it survive our incoming-inspection lab?"
// 06 · OEM EVALUATOR
Component selection at scale
"Does this component meet our spec at 20 Ah, or only at the 200 mAh coin-cell level?"
Six different jobs, six different deadlines, six different deck formats. One shared question: "Is the underlying physics real enough for me to act on?"
What they all actually want
They want three things, in this order:
A verdict they can defend in a meeting that's already on the calendar.
Citations they can quote if challenged ("according to 821 papers in the corpus, including 148 that directly confirm...").
A resolution path if the verdict is ambiguous - what data point, costing what, on what timeline, would resolve the uncertainty.
Notice what is not on this list. They don't want a numeric "score." They don't want a market-size analysis. They don't want a recommendation to invest or not invest. They want the physics question answered, with the corpus behind it, so they can do the rest of their job.
The shared daily pain
Across all six, the same operational pain shows up:
Physics verification is expensive AND slow AND unreliable through the standard channels. A subject-matter advisor on GLG or Guidepoint costs €600-1,200/hr and requires 5-7 working days to schedule. An in-house PhD on retainer costs €15k+ per month and only covers their narrow domain. An LLM gives an answer in 12 seconds - but the answer changes when you rephrase the question (see last week's post by Joachim).
So the de facto choice is between expensive-and-slow, expensive-and-narrow, or fast-and-unreliable. None of the six readers is happy with that menu.
What we send back, regardless of who asked
The deliverable is the same for all six personas: a 2-document PDF in 48 hours.
The investment memo (3 pages) translates the physics into the investor's language: PROBLEM, RESOLUTION, RISK MATRIX, BUY/HOLD logic, references. The technical pre-screen (2 pages) shows the underlying physics check that produced the memo's verdict: each claim, the rule it triggered, the corpus evidence supporting or contradicting it, the resolution path if ambiguous.
A VC partner reads the memo and skims the pre-screen for IC defensibility. A procurement officer reads the pre-screen first and the memo for executive summary. The artifact is the same - the reading order changes.
What we don't do, and why it matters
I get asked, weekly, to extend the scope. Can you also assess the team? Can you validate the market size? Can you give us a valuation range? Can you predict whether they'll close their Series B in 18 months?
The answer is no, and the reason is in the value prop: we do one thing - physics verification of technical claims, against a 789K-paper corpus, in 48 hours, with a citable PDF as the deliverable. Everything else, you have other tools for. A focused tool beats a generalist tool, in due diligence as in most things.
If you're reading this and recognize yourself in one of the six cards above - that's the daily reality this product is built for. The free preliminary score on the home page is the simplest way to find out whether it would actually help your work.
LD
Luc Durand
PhD Materials Science · CEA Saclay · Univ. Pierre & Marie Curie · Founder & CTO at MatIntel
// Sources & references
Field notes from 25 delivered pre-screens, Q1-Q2 2026 (battery domain, anonymized at company level)
GLG / Guidepoint published advisor rate cards (Q1 2026 industry benchmarks)
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