CD ComputaBio provides self-assembly simulation services to explore the spontaneous organization of molecules into ordered structures—from micelles and vesicles to fibrils, bilayers, and nanocomposites. We combine coarse-grained, atomistic, and dissipative particle dynamics to predict morphology, kinetics, and thermodynamic drivers for materials, formulations, and biomolecular systems.
We predict equilibrium and non-equilibrium aggregate morphologies including spheres, rods, vesicles, lamellae, bicontinuous phases, and fibrous networks using DPD, coarse-grained MD, and Monte Carlo.
We calculate potential of mean force, aggregation number, critical micelle concentration (CMC), and solvation free energy contributions using umbrella sampling and thermodynamic integration.
We simulate self-assembly kinetics using coarse-grained MD, replica exchange, and milestoning to capture nucleation, growth, annealing, and structural rearrangement events.
We model mixtures of surfactants, polymers, peptides, lipids, and cargo molecules to study encapsulation, phase separation, and ordered structure formation.
We simulate self-assembly at solid surfaces, liquid interfaces, or in confined geometries to guide coating, patterning, and adsorption applications.
We model amyloid fibril formation, peptide amphiphile assembly, DNA origami, and protein nanocage formation using both atomistic and coarse-grained approaches.
DPD is a coarse-grained simulation method that captures the hydrodynamic behavior and mesoscopic structure of soft matter, surfactants, polymers, and biological membranes over large length and time scales.
We combine MARTINI, SIRAH, and explicit atomistic force fields to capture molecular interactions, packing, and dynamics with the resolution needed to interpret experimental data.
We apply advanced sampling techniques to overcome energy barriers, sample conformational space, and compute free energy landscapes for complex assembly processes.
Define molecular components, concentrations, solvent conditions, and simulation box. Build coarse-grained or atomistic models with appropriate parametrization.
Randomly place molecules or use pre-assembled seeds, then equilibrate with energy minimization and NPT/NVT simulations to remove artifacts.
Run DPD, CGMD, or atomistic simulations over relevant timescales—from nanoseconds to microseconds—to capture assembly events and structural evolution.
Characterize aggregates by size, shape, aggregation number, internal order, surface composition, and cargo distribution using clustering and order parameters.
Compute potential of mean force, CMC, aggregation free energy, and rate constants using umbrella sampling, metadynamics, or kinetic network models.
Deliver a comprehensive report with assembly diagrams, free energy profiles, morphological maps, and recommendations for formulation or molecular design.
| Project challenge | Recommended simulation | Key outputs | Decision support |
|---|---|---|---|
| Design a drug delivery nanocarrier (micelle/vesicle) | DPD + CGMD for morphology, CMC calculation | Morphology diagram, aggregation number, encapsulation stability | Select lipid/surfactant ratio & chain length |
| Optimize peptide amphiphile assembly | Atomistic & CGMD, REMD for conformation sampling | Fibril vs. micelle preference, beta-sheet content | Guide peptide sequence modification |
| Understand polymer phase behavior in solution | DPD + Flory-Huggins parameter mapping | Phase diagram, domain size, copolymer morphology | Choose polymer composition & concentration |
| Predict assembly kinetics of protein nanocages | CGMD, milestoning, or kinetic Monte Carlo | Assembly pathway, yield, intermediate structures | Optimize assembly conditions & mutants |
We combine simulation expertise with colloid, polymer, and biomolecular physical chemistry to translate molecular structure into predictable assembly behavior. Our workflows are validated against experimental data and tailored to your material or formulation target.
DPD typically covers 10–100 nm and µs–ms; coarse-grained MD covers 10–100 nm and ns–µs; atomistic MD covers 1–10 nm and ns–µs. We can combine methods to bridge scales.
We validate against experimental data such as DLS, SAXS, cryo-EM, TEM, or CMC measurements when available. We also use experimental design to suggest testable hypotheses.
Yes. We incorporate electrostatic interactions in DPD, CGMD, and atomistic simulations with appropriate treatment of salt, pH, and ionic strength.
Yes. We model mixtures of surfactants, polymers, lipids, peptides, drugs, and solvents with custom parametrization for cross-interactions.
A standard project with DPD or CGMD for 3–5 compositions takes 2–4 weeks. More complex studies with free energy calculations or kinetics may take 4–6 weeks.
Share your molecular building blocks and target structure. Our team will design a simulation workflow that delivers morphological, thermodynamic, and kinetic insights for your formulation or material design.
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