Science-Based AI
NobleAI’s Innovative Approach to Practical AI
Get a demoCurrent Innovation Paradigms Don’t Deliver for Chemistry & Energy
Empirical Experimentation
- Does not work well for energy-related applications
- Very slow and expensive
- Does not extrapolate well or provide insight or guidance
Traditional Simulation
- Can’t handle multiple scales & physical laws in chemistry
- Each simulation cycle is slow and costly
- Time/cost increases dramatically with project complexity
Conventional ML
- Requires massive amounts of data, which is not available in chemistry
- Poor/missing data inevitably leads to poor/biased outcomes
- Error-prone and unreliable
Something New is Needed: NobleAI's Science-Based AI
General purpose AI falls short in chemicals and energy. Real systems span molecular interactions, material behavior, equipment and environmental limits, and economic, regulatory, and customer requirements. With experimental data often scarce, NobleAI’s SBAI models encode scientific laws and domain knowledge into machine learning to enrich limited data and deliver accurate predictions with less data than traditional ML. Purpose built for product and process development.
Capabilities of SBAI:
- Custom Ensemble Models built for each use case, not generic.
- Science-Integrated approach that solves complex, multi-scale problems.
- Data-Efficient & Secure predictions with strict privacy and IP protection
- Fast, Scalable Insights across molecules, materials, formulations, and processes.
What Challenges Could be Solved with Science-Based AI
Material Developments
Identify best materials & design materials with desire properties
Formula Optimization
Select & balance ingredients based on various factors, including cost
Device Performance
Optimize designs for performance, life-span, manufacturing, and operating conditions
System Management
Predict system response to changing operating conditions and inputs
Process Optimization
Rapidly optimize operations and process parameters
Why SBAI is Different
Feature
Tradtitional ML
Generative AI
SBAI
Data requirements
Large, structured
Massive, often external
Uses minimal, targeted data
Accuracy in science
Requires extensive training
Often speculative
Scientifically rigorous, built for R&D
Model Transparency
Varies
Opaque (black box)
Explainable, integrates scientific principles
Use in chemical R&D
Limited
Risky (hallucinations, unreliable)
Designed to address real-world R&D challenges
From Labs to Laptops.
From Months to Minutes.
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Next Steps
