8.3 Multiscale Modeling

Multiscale modeling bridges different scales in chemical engineering, from quantum mechanics to continuum models. It allows us to simulate complex systems by combining detailed atomic-level descriptions with efficient large-scale representations.

Coarse-graining simplifies molecular systems, enabling simulations of larger systems and longer timescales. Techniques like Iterative Boltzmann Inversion and Force Matching help create accurate coarse-grained models that capture essential features of the original system.

Multiscale Modeling Principles and Techniques

Principles of multiscale modeling

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• Multiscale modeling bridges different length and time scales
  • Quantum mechanics describes electronic structure and chemical reactions at the atomic level (angstroms and femtoseconds)
  • Molecular dynamics simulates the motion of atoms and molecules over short time scales (nanometers and nanoseconds)
  • Mesoscale methods provide coarse-grained representations of molecular systems, reducing complexity while preserving essential features (micrometers and microseconds)
  • Continuum models capture macroscopic behavior using partial differential equations (millimeters and seconds)
• Hierarchical coupling of models across scales
  • Information is passed between models at different scales in a sequential manner
  • Lower-scale models (quantum mechanics) inform parameters for higher-scale models (molecular dynamics)
  • Enables efficient simulation of multiscale phenomena by leveraging the strengths of each modeling approach
• Concurrent coupling of models across scales
  • Models at different scales are solved simultaneously and exchange information during the simulation
  • Coupling is achieved through boundary conditions that ensure consistency between the scales
  • Allows for dynamic feedback between scales, capturing complex interactions and emergent behavior

Coarse-graining for complex systems

• Coarse-graining reduces the degrees of freedom in a molecular system
  • Groups of atoms are represented by single interaction sites, simplifying the system
  • Reduces computational cost while retaining essential features relevant to the phenomena of interest
  • Enables simulation of larger systems and longer time scales compared to atomistic models
• Systematic coarse-graining methods
  1. Iterative Boltzmann Inversion (IBI)
     • Derives effective potentials for coarse-grained interactions
     • Iteratively adjusts potentials to reproduce radial distribution functions from atomistic simulations
     • Ensures that the coarse-grained model captures the structural properties of the original system
  2. Force Matching (FM)
     • Minimizes the difference between forces in the atomistic and coarse-grained models
     • Determines coarse-grained potentials that best reproduce the forces acting on the interaction sites
     • Provides a systematic way to parameterize coarse-grained models based on atomistic data
• Structure-based coarse-graining
  • Martini model widely used for biomolecular systems (lipids, proteins)
    • Maps four heavy atoms to one coarse-grained bead, reducing the number of particles
    • Parameterized to reproduce thermodynamic properties such as partitioning free energies
    • Enables efficient simulation of large-scale biomolecular processes (membrane dynamics, protein-lipid interactions)

Multiscale Modeling Integration and Analysis

Integration of modeling techniques

• Quantum Mechanics/Molecular Mechanics (QM/MM)
  • QM describes the reactive region where chemical reactions occur (active site of an enzyme)
  • MM captures the surrounding environment using classical force fields (protein and solvent)
  • Coupling through electrostatic embedding (QM region polarized by MM charges) or boundary region (link atoms)
  • Enables accurate modeling of chemical reactions in complex environments
• Atomistic-to-Continuum (AtC) coupling
  • Molecular dynamics used in regions with high gradients or fluctuations (near interfaces or defects)
  • Continuum models (finite elements) used in bulk regions with smooth fields (elastic deformation)
  • Coupling through overlapping domains (handshake region) or hybrid elements (Arlequin method)
  • Allows for seamless integration of atomistic and continuum descriptions in a single simulation
• Equation-free multiscale methods
  • Microscopic simulators (molecular dynamics) used as "black boxes" without explicit governing equations
  • Macroscopic equations derived from short bursts of microscopic simulations (coarse projective integration)
  • Enables multiscale modeling when the macroscopic equations are unknown or difficult to derive

Analysis across scales

• Extracting macroscopic properties from microscopic simulations
  • Stress-strain curves obtained from molecular dynamics simulations of materials under deformation
  • Transport coefficients (diffusivity, viscosity) calculated using Green-Kubo relations based on equilibrium fluctuations
  • Bridges the gap between microscopic simulations and macroscopic properties relevant for engineering applications
• Identifying emergent phenomena and structure-property relationships
  • Self-assembly of nanostructures (micelles, vesicles) from molecular building blocks
  • Phase transitions and critical phenomena (order-disorder transitions, phase separation) emerging from collective behavior
  • Relates the microscopic structure and interactions to macroscopic properties and functionality
• Uncertainty quantification and sensitivity analysis
  • Propagation of uncertainties across scales using stochastic methods (Monte Carlo, polynomial chaos)
  • Identification of key parameters and dominant mechanisms through sensitivity analysis (Sobol indices)
  • Assesses the reliability and robustness of multiscale predictions in the presence of uncertainties
• Validation and comparison with experimental data
  • Multiscale simulations guide the design of experiments by predicting critical conditions or optimal parameters
  • Experimental results (microscopy, spectroscopy) validate and refine multiscale models
  • Iterative feedback between simulations and experiments improves the accuracy and predictive power of multiscale approaches