The Intelligence Infrastructure for the Materials-Critical Decade
AI hardware. Quantum systems. Defense electronics. Clean energy. The next wave of technological competition will be won or lost at the materials layer — and by the time integration failures surface today, billions are already committed.
Materis generates System Integration Readiness scores using multi-manifold Graph Neural Networks trained across 34,000+ materials — 6 months ahead of plan. The result: stack-level integration failure made visible before the capital is committed.
R&D teams, advanced manufacturers, and government programs all touch the materials stack — but none of them share a common language for integration risk. Materis is that language.
Every materials decision your organization makes leaves a signal. Materis captures it, reasons across it, and gets smarter with every run — so the next decision is faster, cheaper, and more defensible than the last.
Why Materis Exists
Advanced materials programs fail late and expensively — not because the science was wrong, but because system-level integration constraints across the full stack are invisible until scale-up begins.
Thermal expansion mismatches, diffusion barriers, mechanical stress coupling, electrical compatibility, process history, and supply chain realities don't show up in lab results. They show up after hundreds of millions in fab investment. And for too long, engineers attributed those failures to process variation — because the diagnostic category for stack-level incompatibility didn't exist.
Materis was built to create that category. System Integration Readiness scores. Provenance-tracked reasoning. A platform that compounds with every run. The infrastructure that makes materials integration failure visible, auditable, and preventable — for the first time.
Real-World Evaluations
Candidate materials for quantum device architectures were evaluated against processing constraints and thermal stability thresholds — surfacing integration risks that traditional lab characterization wouldn't catch until high-volume manufacturing.
01
Quantum Computing Materials
02
Semiconductor Interconnect Systems
Integration constraints across advanced interconnect stacks were evaluated at the nanoscale interface — identifying thermal expansion mismatches, diffusion barrier failures, and electrical compatibility risks before fab investment is committed.
03
PFAS Decomposition Pathways
Materis reasoned across PFAS mitigation and decomposition pathways — mapping feasibility constraints and environmental tradeoffs bidirectionally, from materials creation through lifecycle impact.
How Materis Works
Materis reasons from atomic structure to deployment decision through a layered architecture — and compounds in intelligence with every run.
Atomic Intelligence Layer
Multi-manifold Graph Neural Networks trained across 20,900+ materials reason at the atomic, molecular, and structural scale simultaneously — capturing the physics of how materials actually behave, not just how they're categorized.
Evidence & Knowledge Graph
Scientific literature, experimental data, and operational history are linked through provenance-tracked knowledge graphs. Every claim is traceable to its source. Every source is weighted by credibility.
Cross-Scale Reasoning Engine
Integration constraints are evaluated across the full stack — thermal expansion, diffusion barriers, mechanical stress coupling, electrical compatibility, process history, and supply chain realities — simultaneously, not sequentially.
SIR Scoring Engine
System Integration Readiness scores are generated with confidence weighting and traceable rationale — giving teams an auditable, defensible basis for materials decisions before capital is committed.
Scientific Memory Layer
Every stack evaluation compounds into the platform's knowledge base. Materis gets smarter with every run — so your organization's materials intelligence accumulates rather than evaporates.
FOUNDER
Built by Someone Who Lived the Problem
Dr. Melissa Fortuna | Founder & CEO
Twelve years at Intel across the 22nm, 14nm, and 10nm node ramps. PhD in Materials Science from Vanderbilt University, where her research focused on gradients in quantum materials — the same physics that underlies Materis's approach to integration failure prediction.
The insight behind Materis didn't come from a whiteboard. It came from watching a cobalt interconnect program retreat at 10nm — and realizing that the failure wasn't process variation or equipment drift. It was a missing diagnostic category. Engineers couldn't see stack-level integration failure because the framework to name it didn't exist.
Materis was built to create that framework. Not as an academic exercise — as infrastructure for the organizations still making billion-dollar materials decisions the way Intel was making them fifteen years ago.
The Urgency of Materials Intelligence
The next decade of technological competition — in AI hardware, quantum systems, defense electronics, and clean energy — will be won or lost at the materials layer.
Yet the tools used to make materials deployment decisions are still fundamentally pre-AI: expert intuition, spreadsheets, and late-stage physical testing. By the time failures surface, billions have already been committed.
Materis exists because the diagnostic category for materials integration failure didn't exist. Now it does.
Contact Us
Interested in working together? Fill out some info and we will be in touch shortly. We can’t wait to hear from you!