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Overview
Case Studies
Plastics
Sustainable Packaging Revolution: High-performance bio-based barrier polymers
The Customer
A Fortune 500 packaging materials company ($10B revenue) needed to respond to mounting regulatory pressure and customer demand for sustainable alternatives. With the $350B packaging industry facing plastic waste regulations and current bio-plastics showing 3-10x higher oxygen permeability than PET, they risked losing market share in the $45B food packaging segment.
Market Context
Consumer brands paying 20-40% premiums for sustainable packaging. Current bio-plastics fail FDA barrier requirements for carbonated beverages. Performance-cost gap: bio-based alternatives cost 2-5x conventional plastics. Timeline: Traditional R&D requires 18-24 months
The Problem
The customer sought to develop a bio-based barrier polymer matching PET performance for food packaging applications—a project estimated at 18-24 months using traditional methods. They faced a critical market window: major consumer brands were selecting sustainable packaging suppliers within 12 months.
Critical Requirements:
- Oxygen transmission rate <1.0 cc·mil/m2·day·atm (match PET)
- Water vapor transmission rate <0.5 g·mil/m2·day
- Tensile strength >50 MPa; Glass transition temp: 70-85°C
- Biodegradation >90% in 180 days (ASTM D6400)
- FDA food contact approval pathway <18 months
- Processing on existing PET lines (temperature <280°C)
- Cost target: <150% of PET pricing at scale
The Process
In the first month, the customer rapidly onboarded to the N-ERGY Platform. Within their first four months, they leveraged the integrated workflow:
1. LLM Co-Pilot & Data Curation: Agent-driven pipelines compiled 2,000+ bio-polymer structures with oxygen transmission rates from a commercial database, patent literature, and several open source materials databases. Natural language project setup defined precise performance targets across
technical and business dimensions.
2. Multi-Scale Simulation: Computational models screened bio-monomer combinations from commercially available feedstocks using molecular dynamics simulations. AI models predicted barrier properties (OTR, WVTR) and biodegradation pathways, successfully identifying structure-property relationships. Platform analyzed 12,000+ polymer-property relationships using transformer neural networks.
3. AI-Driven Optimization: Multi-objective genetic algorithms optimized across five critical business dimensions: (1) technical performance, (2) synthesis feasibility, (3) economic viability (material cost, processing, capital), (4) sustainability (carbon footprint, toxicity, compliance), (5) supply chain security. The system identified that furanoate-sebacate copolyesters offered optimal balance—a non-obvious combination requiring simultaneous optimization across competing objectives.
4. Experimental Validation: Over the next three months, the platform autonomously generated candidates and guided experimental validation. The customer achieved breakthrough formulation ratio meeting all specifications.
The Outcome
✅ Within 4 months, the customer identified validated candidates achieving OTR 0.8cc·mil/m2·day·atm—matching PET barrier performance while maintaining 94% biodegra-
dation in 160 days (marine environment).
✅ Timeline acceleration of 4.5-6x (4 months vs 18-24 months traditional) enabled participation in major consumer brand supplier selection—capturing $200M contract that traditional timeline would have missed entirely.
✅ The customer established data architecture around N-ERGY’s framework and initiated rapid expansion to three additional sustainable materials programs across packaging portfolio.
KPI & Business Impact
Strategic Business Value
1. Captured $200M revenue opportunity by meeting 12-month supplier selection window that traditional timeline would have missed
2. 15-25% first-mover market share in $45B sustainable food packaging segment vs <5% for late entrants
3. Avoided 20-40% price premium penalty through performance parity enabling competitive pricing.
4. Established platform capability now deployed across 3 additional sustainable materials programs
5. Strategic positioning for upcoming EU Single-Use Plastics Directive and similar regulations
This case study demonstrates the critical business imperative for rapid materials innovation in sustainability-driven markets, where traditional 18-24 month development cycles guarantee market exclusion while AI-accelerated platforms enable capture of first-mover advantages worth hundreds of millions in revenue.
Speciality Chemicals
Accelerating PFAS-Free Aerospace Sealants: A breakthrough in materials development
The Customer
A global top 5 aerospace manufacturer needed to retain their innovation advantage across their sealant portfolio. With 85% of PFAS formulations becoming obsolete by 2026, they faced $450M in revenue risk. After trying to build AI capability internally and finding it difficult to scale, they turned to N-ERGY.
Market Context
The $2.3B aerospace sealant market faces regulatory PFAS bans by 2026. Current alternatives show 40-60% reduced thermal stability, risking qualification delays and production losses. Timeline: Traditional R&D requires 18-24 months
The Problem
In their first application of the N-ERGY Platform, the customer sought to develop a high-performance aerospace sealant to replace PFAS while maintaining critical performance—a project estimated to span 18-24 months. The customer had already made significant investment into the project, and yet prior to using the N-ERGY Platform, they struggled to achieve the breakthrough necessary to meet their targets.
Critical Requirements:
- Service temperature >300°C with 10,000+ thermal cycles
- Tensile strength >12 MPa at elevated temperature
- Fuel and hydraulic fluid resistance
- Regulatory compliance and qualification documentation
The Process
In the first month, the customer quickly on-boarded and trained to use the N-ERGY Platform autonomously. Within their first three months, they leveraged the integrated workflow:
1. LLM Co-Pilot & Data Curation: Agent-driven pipelines curated 5,000+ sealant records including patent and regulatory data, with natural language project setup defining precise performance targets.
2. Multi-Scale Simulation: Computational models predicted fluorine-free copolymer properties (silicone-polyimide formulations) with >80% confidence rankings. AI models screened millions of ingredient combinations as surrogate for DFT/MD calculations, successfully narrowing candidates down to 30 viable options.
3. Robotic Automation: Automated synthesis and multi-modal characterization workflows were dynamically optimized in our lab, with real-time analytics triggering adaptive experimentation.
4. AI-Driven Optimization: Over the next month, the platform autonomously generated property predictions (Tg, tensile strength, thermal stability) with self optimized Random Forest models for glass transition temperature. The system identified that mixing BPDA with PMDA dianhydrides significantly enhanced mechanical properties—a non-obvious combination.
The customer achieved breakthrough formulation (BPDA/PMDA 50/50 + ODA/DSX 90/10) meeting all specifications. The number of validated candidates and projects accelerated significantly through the platform’s closed-loop workflow.
The Outcome
✅ Within the first 3-4 months, the customer identified the breakthrough candidate that would successfully meet performance targets without PFAS, impacting much of their product catalog.
✅ The customer team estimated they would now be able to get the new formulation through qualification in 3-4 months rather than the 18-24 months originally planned— 80% faster.
✅ The customer built data architecture around N-ERGY’s GEMD (Graphical Expression of Materials Data) schema and planned for subsequent data integration alongside rapid enterprise expansion.
KPI & Business Impact
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Strategic Business Value
1. Avoids $450M losses by preserving engine production schedule
2. Frees 5+ FTE-years through automation and AI-guided workflows
3. Secures 12+ month competitive lead in PFAS alternatives market
4. Future-proofs R&D with modular, extensible AI-driven platform
This case study highlights the significant business and technical benefits achievable by integrating agentic AI, multi-scale simulation, automated experimentation, and real time decision support in specialty polymer development under critical regulatory pressures.
Extreme Environment Materials
Next-Generation Thermal Barrier Coatings: Computational breakthrough for high-temperature aerospace
The Customer
A global top 3 aerospace engine manufacturer needed next-generation thermal barrier coatings to break the 1350°C temperature limit constraining turbine efficiency. With 5-7 year traditional development timelines threatening competitive position in next-gen engine programs, they turned to N-ERGY after 18 months of conventional screening yielded limited progress.
Market Context
A global top 3 aerospace engine manufacturer needed next-generation thermal barrier coatings to break the 1350°C temperature limit constraining turbine efficiency. With 5-7 year traditional development timelines threatening competitive position in next-gen engine programs, they turned to N-ERGY after 18 months of conventional screening yielded limited progress.
The Problem
In their first deployment of the N-ERGY Platform, the customer sought to develop high-temperature thermal barrier coatings enabling >1400°C operation—a critical breakthrough for next-generation turbine efficiency. After 18 months of conventional empirical screening with minimal progress, they faced timeline pressure that traditional R&D approaches could not address.
Critical Requirements:
- Service temperature >1400°C (current limit: 1350°C)
- Thermal conductivity <2 W/m·K at 1200°C
- Thermal shock resistance >1000 cycles (1400°C to 300°C)
- Phase stability: No deleterious phases for >50,000 hours
- Bond coat adhesion >40 MPa; ASTM C633 compliance
- Cost target: <$500/kg ceramic powder
The Process
Over 9 months, the customer leveraged the integrated N-ERGY Platform workflow:
1. LLM Co-Pilot & Data Curation: Automated pipelines aggregated 4,467 high-temperature ceramics from Materials Project (ICSD-verified), 6,000+ thermal properties from AFLOW database, 38 experimental validations from NIST, and processed 850 research papers extracting 250+ rare-earth ceramic compositions with performance metrics. Natural language project setup defined precise temperature and conductivity targets.
2. Multi-Scale Simulation: Graph neural networks and ensemble models predicted thermal conductivity and thermal expansion for all candidates, screening to 87 meeting criteria. CALPHAD thermodynamic modeling analyzed phase stability across temperature ranges. Custom TDB file development overcame database limitations for rare-earth oxide systems.
3. AI-Driven Optimization: The platform identified a high-entropy composition exploiting configurational entropy for enhanced stability and reduced thermal conductivity. This non-obvious 7 component system emerged from AI ranking across technical performance, phase stability, and cost dimensions simultaneously.
4. Experimental Validation: Targeted synthesis of 12 top candidates via co-precipitation and plasma spray deposition, with comprehensive characterization (XRD, SEM, thermal conductivity, thermal cycling). Real-time analytics validated computational predictions with 86% accuracy, enabling rapid iteration.
The customer achieved breakthrough TBC meeting all specifications through systematic computational-experimental integration, dramatically outperforming traditional empirical approaches.
The Outcome
✅ Within 4 months, the customer identified validated candidates achieving OTR 0.8cc·mil/m2·day·atm—matching PET barrier performance while maintaining 94% biodegra-
dation in 160 days (marine environment).
✅ Timeline acceleration of 4.5-6x (4 months vs 18-24 months traditional) enabled participation in major consumer brand supplier selection—capturing $200M contract that traditional timeline would have missed entirely.
✅ The customer established data architecture around N-ERGY’s framework and initiated rapid expansion to three additional sustainable materials programs across packaging portfolio.
KPI & Business Impact
Strategic Business Value
1. Secures $1.4B annual fuel savings potential through 70°C temperature increase enabling 2.8% efficiency gain industry-wide.
2. Captures next-gen engine program position with 4-5 year competitive lead vs. traditional development timelines.
3. Reduces maintenance costs by $15-30M per airline annually through 30% improvement in coating failure rates.
4. Enables rapid materials substitution for supply chain resilience—can respond to rare earth disruptions in months vs. years.
5. Unlocks multi-application leverage—expanded to 4 business units (battery separators, catalyst supports, electronic ceramics)
This case study demonstrates how integrated computational materials design—combining automated data aggregation, multi-scale simulation, AI-driven optimization, and targeted experimental validation—transforms materials R&D from a multi-year bottleneck into a rapid competitive advantage, achieving order-of-magnitude timeline acceleration with dramatic cost reduction.