Co-Founder & CEO
Profile summary: PhD in Nuclear Engineering at Massachusetts Institute of Technology
N-ERGY
N-ERGY utilizes an AI-driven material recommender that analyzes over 40 million simulations to identify optimal materials for applications in nuclear energy, electronics, and aerospace. This technology reduces the material search space by 99%, significantly accelerating research and development timelines for advanced engineering projects.
- Artificial Intelligence
- Data & Analytics
- Chemical Technology
- New Materials
Updated 7 months ago
Funding
Raised to date
$1.5MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
across 1 round
$1.5M · Seed
Announced Jan 2024
Disclosed cumulative: $1.5M
RoundDateAmountValuationInvestors
Founders
Product
Problem
The process of identifying optimal materials for advanced engineering applications in nuclear energy, electronics, and aerospace is traditionally slow and resource-intensive, involving the evaluation of a vast number of potential candidates. This extensive search space significantly prolongs research and development timelines.
Solution
N-ERGY offers an AI-driven material recommender that drastically accelerates material discovery by analyzing data from over 40 million physics-based computational simulations and physically tested materials. The AI model narrows down billions of material possibilities to a curated list of top candidates tailored to specific product requirements. This technology reduces the material search space by 99%, enabling faster identification of materials that enhance efficiency, stability, and longevity in demanding applications. The platform provides instant material recommendations, empowering experts to save time, effort, and resources in material discovery.
Target Audience
The primary customers are researchers, engineers, and product developers in the nuclear energy, electronics/semiconductors, aerospace, and automotive industries seeking to accelerate material discovery and optimize material performance.
Features
- AI-powered material selection that analyzes billions of material combinations.
- 99% reduction in material search space compared to traditional methods.
- Recommendations delivered up to 10 times faster, significantly reducing development timelines.
- Exhaustive exploration of potential materials, from unexplored concepts to existing real-world applications.
- Optimized material properties for next-generation nuclear reactors, carbon capture, and renewable energy applications.
- Selection of materials that enhance efficiency, stability, and longevity in electronics and semiconductors.
- Identification of materials that withstand harsh conditions for aerospace and automotive applications.