ENERGY_MATERIALS_AI
AI MaterialsDiscovery&BatteryOptimization
End-to-end platform unifying generative materials models, ML surrogates, and RL optimization for battery design, from discovery to deployment.
MatGPT
PhysicsNeMo
BEEP
MatSci-LLM
Digital Twin
CANDIDATES_GENERATED
12,478
Materials formulas validated
ENERGY_DENSITY_WH/KG
267.3
Target cathode performance
VALIDATION_SPEEDUP_X
10,247
vs traditional DFT methods
ENERGY_TRANSITION_CHALLENGE
The world's energy transition depends on discovering new materials
but conventional R&D is slow, expensive, and fragmented
⏱
Slow
Testing a few hundred candidates takes months of lab time
💰
Expensive
Synthesis, measurement, and DFT computation costs are immense
📊
Fragmented
Data sits across simulations, lab notebooks, and vendor spreadsheets
🔄
Non-reproducible
Differing simulation codes, parameter sets, and lab conditions
🔌
Disconnected
Field insights (aging, degradation, safety) rarely feed back into design
End-to-End Platform Solution
Generative Models
MatGPT
ML Surrogates
Property prediction
RL Optimization
Multi-objective
Digital Twin
Lifecycle mgmt
Web + Mobile
Collaboration
2024–2025 REAL PROJECTS
Anchor Stack
01
MatGPT
2025
Generative transformer trained on 50M materials-science texts
CAPABILITY
Predicting new material formulas and synthesis routes
→
02
Matbench Discovery v2
2025
Large benchmark dataset for materials property prediction
CAPABILITY
Validates ML surrogate accuracy
→
03
BEEP
2025
Battery Evaluation and Early Prediction pipeline
CAPABILITY
Proven production-grade data pipeline
→
04
MatSci-LLM
2025
LLM fine-tuned on materials datasets (crystal structures, SMILES, CIFs)
CAPABILITY
Generative + interpretive capabilities
→
05
NVIDIA PhysicsNeMo
2025
Physics-informed neural networks (PINNs) for solving PDEs
CAPABILITY
Real-time simulation surrogate core
→
06
LiionDB
2025
1.5M+ real battery cycles dataset
CAPABILITY
Enables data-driven aging prediction
→
These projects anchor every stage: generation → simulation → validation → deployment
TENSORBLUE_ENERGY_INTELLIGENCE_STACK
Architecture
01
Material Texts, Papers, Databases
↓
02
GENERATION: MatGPT / MatSci-LLM
↓
03
PREDICTION: ML Surrogates (GraphNets, PINNs)
↓
04
OPTIMIZATION: RL / Multi-objective Bayesian Opt
↓
05
SIMULATION VALIDATION: DFT / MD / FE
↓
06
MANUFACTURING DIGITAL TWIN
↓
07
LIFECYCLE FEEDBACK
↓
08
LLM COPILOT
↓
09
WEB + MOBILE APPS
MODULE_A
Generative Materials Model
GenAI Layer
Input → Output
INPUT
"Generate new cathode materials with >250 Wh/kg, low cobalt, stable up to 4.3V"
↓
OUTPUT
Candidate formulas, crystal structures (CIF), predicted synthesis methods, confidence scores
Techniques
Self-supervised pretraining on materials literature
RAG from Materials Project, PubChem, NREL
Structural decoding with equivariant transformers
Reinforcement fine-tuning with reward optimization
MODULE_B
Property Prediction & Surrogate Models
01
Graph Neural Networks
Bandgap, formation energy, elastic moduli, diffusion coefficients
02
PINNs (PhysicsNeMo)
Li-ion diffusion, temperature gradients, mechanical stress
03
Accelerated Validation
DFT-level accuracy in 0.5 ms instead of 5 hours
04
Uncertainty Quantification
Ensemble dropout; calibrates confidence for each property
Example Performance
Predicting ionic conductivity for solid electrolytes with <2% error vs DFT benchmarks at 10,000× lower compute
MODULE_C
Optimization Layer
Reinforcement + Multi-objective
Reward Function
R = α(EnergyDensity) − β(Cost) − γ(Toxicity) − δ(DegradationRate)
α: energy density weight
β: cost penalty
γ: toxicity penalty
δ: degradation penalty
Approach
Method
Multi-Objective RL (MORL) using PPO + scalarization
Environment
Material property space (continuous)
Output
Top-5 Pareto-optimal candidates
MODULE_D
Simulation & Validation
Physics-Grounded
01
PhysicsNeMo / Modulus PINNs
Replicate DFT or MD results: electrochemical reactions, charge transport, thermal runaway
02
Finite-Element Integration
Coupled with COMSOL, IDAES to validate structural and thermal stability
03
Auto-Calibration
RL agents adjust hyperparameters until residual < 0.01 of baseline simulation
Outcome
Months of HPC time reduced to hours for thousands of candidates
MODULE_E
Digital Twin & Lifecycle Management
01
Manufacturing
Slurry mixing, coating, drying, cell assembly simulation
Anomaly detection (autoencoders on sensor data)
PLC/MES integration for real-time defect detection
02
Operation & Field Data
BEEP and LiionDB datasets for lifetime prediction
Recurrent models (GRU/TCN) predict degradation
Mobile dashboards: SoH, temperature, projected EOL
03
Recycling & Circular Feedback
Track material recovery efficiency
Feed recovered material data into GenAI
Design recyclable chemistries
MODULE_F
LLM Copilot & Web/Mobile Interface
Copilot (EnergyGPT)
Query:
"Show me all cathode candidates with formation energy < −1.8 eV and cost < $30/kg"
Query:
"Summarize degradation causes for LiFePO₄ under high C-rates"
Capabilities
Fine-tuned on materials science + process engineering
RAG with Materials Project, BEEP, Matbench
Executes SQL/Python code snippets behind scenes
Explanations grounded in citations
Web Interface (React/Next.js)
• Dynamic dashboards for candidate ranking
• Drag-and-drop CIF viewer
• Pareto-front visualizer (3D scatter)
Mobile Interface (React Native)
• Field engineers monitor battery pack SoH
• Real-time alerts for anomalies
• Camera OCR for serial numbers
Data Governance & Infrastructure
| LAYER | TECHNOLOGY | PURPOSE |
|---|---|---|
Data Lake | Delta Lake / S3 | Store raw and processed datasets (DFT, experiments, telemetry) |
Vector DB | Milvus / Weaviate | Store embeddings for retrieval (RAG) |
Model Registry | MLflow | Version control for surrogates, RL agents, and LLMs |
Workflow Orchestration | Airflow / Kubeflow | Automate generation → simulation → validation pipelines |
Security | Keycloak + SSO | Access control for scientists / field operators |
Monitoring | Prometheus + Grafana | Track model drift, system health, and inference latency |
Evaluation Metrics
GenAI Candidate Generation
Valid formula ratio
>90% chemically valid
Property Prediction
MAE (energy, bandgap)
<5% vs DFT
Optimization
Pareto diversity
>0.8 coverage
Manufacturing Yield Prediction
F1 score
>0.95
Battery Life Prediction
RMSE (cycles to failure)
<10 cycles
Field Anomaly Detection
False alarm rate
<1%
LLM Response Accuracy
Fact-grounded citation rate
>95%
End-to-End R&D Speed
Time to candidate validation
↓ 70% vs baseline
Case Study
Discovery of High-Energy, Low-Cobalt Cathode
Problem
Find a low-cobalt cathode with >250 Wh/kg, stable above 4.3 V
Pipeline Run
01
MatGPT generates 12,000 formulas → filtered to 300 by stability prediction
02
GNN Surrogates predict formation energy and voltage profiles
03
RL optimizer runs Pareto optimization (energy vs cost vs toxicity)
04
PINN simulator (PhysicsNeMo) evaluates top-10 under stress conditions
05
Experimental validation confirms 2 candidates meet specs
Outcome
+18%
Cycle life improvement
-70%
Cobalt content reduction
-22%
Production cost down
Closed feedback loops
Tensorblue builds closed feedback loops between GenAI discovery, physical validation, and operational intelligence. From a researcher generating formulas to an engineer tracking battery health on a mobile dashboard — every insight flows through one AI stack.
ACTIVE_PROJECTS_2024_2025
MatGPT
MatSci-LLM
BEEP
PhysicsNeMo
Matbench v2
LiionDB
Factual, physics-grounded AI — not speculative LLM output. Integration of R&D + manufacturing + operations. Scalable, explainable, and audit-friendly design.