Simpal AI
How physics-based descriptors power predictive material intelligence.
Simpal AI is the bridge between raw simulation data and AI-supported interpretation. Its primary role is to structure material, formulation, simulation, and experimental information into reusable, standardized representations.
Experimental Data
Rheology, tensile, DMA, thermal
Simulation Data
Stress, RDF, density, mobility
Simpal AI
Physics descriptors, surrogate models, uncertainty
Property Model
Interpolation, ranking
Decision Output
Candidates, risk indicators
Figure 1. Simpal AI Architecture. Experimental and simulation data are converted into physically meaningful material representations before property models are trained or applied.
Knowledge-Graph Readiness
Simpal does not just throw data into a black-box neural network. Instead, it structures simulation and experimental data into a physics-aware descriptor layer.
This means our objective is not only prediction, but creating searchable, reusable, and assumption-traceable material knowledge. When the AI ranks a formulation as "high risk for phase separation," that prediction remains inextricably linked to the underlying morphology descriptors and calibration data.
Data Fusion
The true power of Simpal AI lies in its ability to fuse computational data with physical lab results.
- Simulation Data: Feeds in high-resolution, scale-independent parameters like molecular mobility, interaction energies, and local density fluctuations.
- Experimental Data: Feeds in macroscopic ground-truth calibrations like DMA results, tensile strength, and capillary rheology.
By mapping both into the same descriptor space, Simpal's Property Model can interpolate between known data points with high confidence, identifying new processing windows or formulation alternatives that would have been missed by trial-and-error R&D.
Confidentiality & Pilot Scope
Security is paramount. Client-provided formulations, processing parameters, and validation data are treated as confidential project inputs. They are strictly segregated and are not used for model training outside the agreed pilot scope without explicit permission.