Multiscale modeling for predicting 3D deformation of thin flexible materials

Overview

This innovative multiscale modeling solution leverages a combination of density functional theory (DFT), semiempirical quantum chemical methods, molecular dynamics (MD) simulations, and machine-learning frameworks to predict the 3D deformation of soft, flexible materials. By capturing both molecular interactions and macroscopic mechanical responses, this approach seeks to accurately predict strain distribution, buckling onset, and material conformance. This technology is designed to inform material design, minimizing folding and wrinkling, and providing high-accuracy predictions for experimental validations.

Technical specifications

Core components:

• DFT and Semiempirical Quantum Methods: Provide detailed insights into intermolecular forces and adhesion critical for modeling material behavior.
• Force-field Simulations: Model larger-scale structural stability and interactions within materials.
• Molecular Dynamics Simulations: Analyze dynamics of polymer chain motion and stress distribution under compression.
• Machine Learning Frameworks: Trained on comprehensive datasets to rapidly predict material deformation, enhancing the modeling process with high accuracy.

Validation Process:

• Initial validation on multiscale biological and nanochemical systems demonstrates accuracy in predicting molecular interactions and structural deformations.
• Future validation includes extensive DFT and MD studies, with predictions benchmarked against experimental data.

Technology readiness level

This technology is currently at TRL 3, with successful validation of the multilevel approach in related systems. Further development will focus on refining predictive models and validating against industry-relevant experimental data.