AI-Powered Deployment Confidence for Advanced Materials
Materis builds intelligence infrastructure that predicts whether advanced materials will scale successfully — before costly deployment.
Our AI platform evaluates manufacturing constraints, supply chain risk, performance requirements, and environmental impact to accelerate R&D from concept to production.
We help R&D teams, advanced manufacturing organizations, and government programs identify hidden risks early, prioritize viable pathways, and accelerate materials innovation with confidence.
Interactive research prototype. Outputs generated using a multi-manifold graph neural network trained on Materials Project and JARVIS datasets.
Why Materis Exists
Advanced materials programs fail late and expensively — not because discovery was wrong, but because processing, manufacturing, safety, and supply constraints are often invisible until scale-up begins.
Materis was created to bring those constraints forward in the development lifecycle. By integrating data and models across materials, processing assumptions, and production realities, we aim to support earlier, better-informed decisions about which innovation paths are viable — and which should be abandoned before costly physical investment.
Our mission is to help organizations invest more intelligently in materials innovation, reduce late-stage failures, and accelerate the transition from promising ideas to deployable technologies.
Real-World Evaluations
Materis evaluated candidate materials for quantum device architectures, identifying processing constraints and stability risks that could affect scalability from R&D stacks to high-volume manufacturing. We pave the way for earlier prioritization of viable materials before the production investment.
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Quantum Computing Materials
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Solid-State Storage Components
We assessed advanced materials used in high-performance storage systems, highlighting manufacturability and supply chain sensitivities that could impact reliability and production scale. These insights help organizations anticipate bottlenecks before deployment.
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PFAS Decomposition Pathways
Materis analyzed potential pathways for PFAS mitigation and decomposition, identifying feasibility constraints and environmental tradeoffs. This demonstrates bidirectional reasoning across materials creation, usage, and lifecycle impact.
How Materis Works
Materis integrates atomic-scale reasoning with real-world deployment intelligence through a layered architecture.
Atomic Intelligence Layer
Physics-informed AI models trained on materials data to reason across atomic, molecular, and structural scales.
Evidence & Knowledge Graph
Curated scientific literature, experimental data, and operational insights linked through provenance-tracked knowledge graphs.
Cross-Scale Reasoning Engine
Evaluates interactions between materials properties, manufacturing processes, supply chains, and environmental constraints.
Decision Intelligence Layer
Generates confidence-weighted assessments with traceable rationale and risk indicators.
Deployment Confidence Framework
Provides actionable insights on feasibility, scaling risk, and strategic pathways forward.
FOUNDER
Built by Materials Practitioners
Dr. Melissa Fortuna
PhD, Materials Science – Vanderbilt University
Former semiconductor manufacturing practitioner (Intel)
Supply chain and advanced materials deployment expertise
Materis is built from firsthand experience with the gap between laboratory breakthroughs and real-world deployment. Our approach combines deep materials science expertise with modern AI infrastructure.
The Urgency of Materials Intelligence
Advanced materials are critical to semiconductor innovation, energy and climate technologies, defense and national security, sustainable manufacturing, and many other industries.
At the same time, development costs, supply chain complexity, and performance uncertainty are increasing. Organizations need better decision intelligence before committing capital to new materials.
Materis addresses this gap.
Contact Us
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