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Overview
Case Studies
Plastics
Capturing $200M by Delivering Sustainable Packaging in 4 Months
The Challenge
A $10B Fortune 500 packaging giant faced pressure to adopt sustainable alternatives, but current bio-plastics had oxygen barriers 3-10× worse than PET. With brands choosing sustainable suppliers within a year, slow innovation risked $200M loss.
What was at stake:
- Consumer brands will pay 20-40% premiums for sustainable options that actually work. But the supplier selection window closes in 12 months
Critical Requirements
- Match PET oxygen barrier (<1.0 cc·mil/m²·day·atm) while staying biodegradable
- >90% breakdown in 180 days
- Use existing production equipment
- Keep costs within 150% of PET at scale
- Maintain mechanical strength above 50 MPa
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Traditional Approach
Conventional bio-polymer development involves a lengthy, step-by-step process resulting in 18-24month development cycles:
- Researchers spend weeks manually searching through scattered literature
- Developing a single formulation from concept to barrier testing takes several months
- Performance, cost, and sustainability are optimized separately, producing materials that may perform well technically but are not economically viable
- There is no structured method to effectively manage the challenging tradeoffs involved
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The Results with N-ERGY AI
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How N-ERGY Compressed Years Into Months
Month 1: Building Intelligence
- N-ERGY’s platform integrated over 2,000 bio-polymer structures with barrier data, automated patent and literature processing, and converted business needs into computational searches. This laid a foundation for strategic exploration.
Month 2-3: Exploring Combinations
- Moving beyond traditional R&D, the platform analyzed 12,000+ polymer-property relationships using molecular simulations, transformer neural networks, and genetic algorithms optimizing performance, manufacturability, cost, sustainability, and supply chain reliability.
- The breakthrough: A unique copolyesters emerged as the optimal, non-obvious solution only found via holistic multi-objective optimization.
Month 4: Success Demonstrated
- Instead of 150–200 experiments, only 45 targeted tests confirmed the top candidates. The resulting bio-based polymer surpassed goals with 0.8 cc·mil/m²·day·atm oxygen transmission and 94% biodegradation within 160 days - exceeding all targets.
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Why N-ERGY's Integrated Platform Made the Difference
The packaging company didn't need another incremental improvement—they needed a material that satisfied impossible-seeming requirements simultaneously, and they needed it before the market closed the door. N-ERGY delivered this by:
- Replacing sequential guesswork with parallel exploration of 12,000+ possibilities
- Discovering non-obvious solutions that emerge only when all constraints are optimized together
- Cutting experimental waste by 77% through intelligent prediction
- Moving at market speed instead of research speed
In sustainability markets, being first with a working solution isn't just valuable, it's the difference between winning $200M contracts and explaining to stakeholders why you weren't ready when customers were buying.
Speciality Chemicals
80% Faster To Market - How A Global Aerospace Leader Solved PFAS Elimination in 4 Months
The Challenge
A leading global aerospace manufacturer ranked among the top five faced $450 million in revenue risk due to 85% of their PFAS-based sealant formulations becoming obsolete by 2026. Despite substantial internal investments, they struggled to achieve the breakthrough needed to meet their targets.
Critical Requirements
- Service temperature exceeding 300°C with over 10,000 thermal cycles
- Tensile strength greater than 12 MPa at elevated temperatures
- Resistance to fuel and hydraulic fluids
- Complete regulatory compliance documentation
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Traditional Approach
Traditional Approach Typically Requires 18-24 Months:
- Manual review of thousands of patents and research papers
- Trial-and-error formulation with minimal computational support
- Sequential experimental cycles with weeks between each iteration
- Data stored in isolated spreadsheets, hindering pattern recognition across experiments
- Expert reliance on simulations, accessible to only 10-15% of the team
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The Results with N-ERGY AI
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The N-ERGY Solution: Platform-as-a-Service Delivery in 3-4 Months
Month 1: Integration & Foundation
What N-ERGY Did:
- Integrated customer's existing simulation data and experimental records into unified platform architecture
- LLM Co-Pilot automatically curated 5,000+ sealant records from patents and regulatory databases
- Established performance targets through natural language interface
Month 2-3: AI-Driven Discovery Loop
Intelligent Screening
- AI models used customer's DFT simulations to screen millions of ingredient combinations
- Narrowed from millions → 30 viable candidates with >80% confidence
Automated Experimentation
- Robotic synthesis and characterization in N-ERGY's lab
- Real-time analytics triggered adaptive experiments
- Closed-loop: Each experiment automatically refined computational models
Autonomous Optimization
- AI continuously predicted thermal and mechanical properties
- Non-obvious discovery: Platform identified novel polymer combinations the customer hadn't considered
Month 4: Breakthrough Delivered
Customer Specifications Met
- PFAS-free formulation meeting all specifications - ready for qualification testing
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Why N-ERGY's Integrated Platform Made the Difference
Traditional approaches keep simulation, experimentation, and data analysis in separate silos with manual handoffs taking weeks.
N-ERGY's platform integrated:
- The customer's existing computational models and simulation data as training inputs
- AI screening of millions of combinations using simulations as surrogates (not replacing physics, but amplifying it)
- Robotic experiments in N-ERGY's lab feeding real-time data back into models
- Autonomous optimization discovering non-obvious material combinations at scale
- Enterprise-ready data architecture enabling rapid replication across their portfolio
The result: breakthrough innovation in months, not years - delivered as a service
Extreme Environment Materials
7x Faster - Breakthrough thermal barrier coatings in 9 months v. 5-7 years
The Challenge
A rapidly growing aerospace engine maker needed advanced thermal barrier coatings exceeding 1350°C to boost turbine efficiency and stay competitive. After 18 months of little progress, their next-gen engine program was at risk.
What was at stake:
- Every 25°C rise saves $500 million annually industry-wide. Coating failures cost airlines $50-100 million yearly.
Critical Requirements
- Operate above 1400°C
- Thermal conductivity under 2 W/m·K
- Endure 1000+ thermal shock cycles
- Stable beyond 50,000 hours
- Cost under $500/kg
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Traditional Approach
Traditional Approach Typically Requires 5-7 Years
- Sequential screening of only 200-500 ceramic options
- Low experimental success rates (15%) mean endless iteration
- Months-long testing cycles between attempts
- Minimal computational tools to guide novel material design
- Fragmented data across isolated experiments
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The Results with N-ERGY AI
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The N-ERGY Solution: 9-Month Platform Deployment
Months 1-3: Data Integration & Computational Sweep
How N-ERGY Accelerated Discovery:
- Automated knowledge extraction from 850 research papers plus major materials databases (Materials Project,AFLOW, NIST)
- Neural network models evaluated thermal properties across 4,467 ceramic candidates
- Advanced thermodynamic simulations assessed phase behavior under extreme conditions
- Intelligent filtering reduced search space to 87 promising compositions
Month 4-6: AI-Powered Innovation
The Breakthrough Moment:
- Platform algorithms discovered a sophisticated 7-element high-entropy ceramic design
- This non-intuitive composition leveraged disorder for enhanced thermal performance
- Simultaneous optimization across cost, stability, and performance metrics
- Final selection: 12 candidates for experimental verification
Month 7-9: Rapid Validation Cycle
Targeted Laboratory Work:
- Precision synthesis and professional coating application
- Complete materials characterization suite
- Predictive models achieved 86% accuracy against physical measurements
- 58% of candidates met all requirements - nearly 4× better than conventional approaches
Outcome: Production-ready coating formulation delivering 1420°C capability and superior thermal insulation
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Why N-ERGY's Integrated Platform Made the Difference
Where traditional methods test hundreds of materials sequentially over years, N-ERGY's integrated approach delivers:
- Intelligent synthesis of disparate knowledge sources into unified analysis
- Computational evaluation of thousands of candidates before lab investment
- Physics-based and ML models working together to reveal unexpected solutions
- Strategic experimental validation achieving 4× higher success rates
- Continuous learning loops where lab results enhance predictive capability
The result: transformative materials innovation delivered as a turnkey service - months instead of years, costing millions (USD) instead of hundreds of millions (USD).