Summary
A high-level overview of the digital simulation lifecycle and customer workflow.
Simpal is a computational screening platform for polymeric materials. The core idea is simple: you provide your formulation details and target conditions, and Simpal runs the underlying molecular modeling and physics simulations to estimate material behavior.
Define Sample
Composition, blend ratios, and test conditions.
Virtual Simulation
3D sample relaxation and molecular dynamics.
Property Estimation
Viscosity curves, moduli, and processing windows.
Figure 1. The digital simulation lifecycle from material specification to virtual testing and property estimation.
How the Workflow Operates
In practice, this process follows three straightforward stages:
- Define Sample: You only need to tell us your material's composition, blend ratios, and target conditions, and we handle the rest of the 3D molecular modeling and setup. For more details, see Virtual Samples.
- Virtual Simulation: Simpal runs virtual tests on the digital sample (such as shear flow or tensile deformation) to observe how chains move and respond under processing conditions. For more details, see Virtual Tests.
- Property Estimation: Simpal translates raw simulation data into practical engineering curves (such as viscosity and viscoelastic response) and processing window indicators to guide physical validation. For more details, see Property Estimation.
What Simpal Needs from You
You do not need to write simulation scripts or manage physics engines. Simpal runs simulations based on standard formulation inputs:
- Material Composition: Information on the materials you use, their composition (such as blend ratios and additives), and any details about the molecular architecture you can provide (like linear vs. branched chains or molecular weights).
- Historical Lab Data (Optional): Past rheological, mechanical, or thermal measurements to help calibrate and ground-truth simulation baselines.
- Testing Conditions: Target temperatures and deformation modes. Specific shear rates are optional, but if you are interested, they can also serve as valuable inputs.
- Target Properties:The specific properties you want to assess (e.g., shear viscosity curves, G'/G'' moduli, blend miscibility).
Data Confidentiality
Your formulation recipes and experimental test data are treated as strictly confidential project inputs:
- No Shared Model Training: We do not use proprietary customer data to train shared or global AI foundation models.
- Dedicated Surrogate Models: Dedicated surrogate models are deployed exclusively for each customer using data from their materials to generate private insights.