What industries does newtsim serve?

newtsim serves oil and gas (flow assurance, drilling fluids, subsea pipeline), pharmaceutical and drug discovery, energy storage (battery materials, electrolytes), semiconductor process simulation, green hydrogen (electrolyser and catalyst design), nuclear decommissioning, aerospace and composites, corrosion and materials, carbon capture, agrochemicals, mining and geotechnical, and fermentation and bioprocessing.

How is newtsim different from standard CFD or simulation software?

Standard simulation tools use empirical models at a single scale. newtsim runs the full physics chain from quantum mechanics and molecular dynamics through to plant-scale CFD and structural mechanics, GPU-native, with direct data flow between every scale and no third-party solver dependencies. Constitutive behaviour is predicted from first principles rather than empirically fitted.

What does a fixed-price simulation study include?

A newtsim fixed-price study has a defined scope, clear deliverables, fixed cost agreed upfront, and is delivered in weeks rather than quarters. Studies typically include a scoping call, physics-grounded simulation using the appropriate newtsim solvers, results analysis, and a written report with actionable recommendations.

What is physics-grounded simulation?

Physics-grounded simulation derives material properties and constitutive behaviour from lower-scale physics (molecular dynamics and quantum mechanics) rather than from empirical fitting. This gives accurate predictions for novel materials and extreme conditions outside the range of experimental data, and enables root-cause analysis at the mechanistic level.

How does newtsim approach oil and gas flow assurance simulation?

newtsim runs a continuous physics chain from molecular wax crystallisation to full pipeline transient multiphase flow, with no empirical handoffs between scales. Wax appearance temperatures, gel strength, and deposition kinetics are predicted from molecular dynamics (newtsim Bond), passed directly to mesoscale morphology (newtsim Pack), and then propagated into multiphase CFD (newtsim Stream), giving flow assurance predictions that hold outside the range of laboratory data.

Can newtsim simulate battery electrolyte behaviour and degradation?

Yes. The Bond solver runs molecular dynamics for Li-ion transport and solid-electrolyte interphase (SEI) formation; Pack handles electrode morphology at the mesoscale; these results couple upward to cell-scale electrochemical models. This gives physics-grounded predictions of electrolyte degradation, capacity fade, and thermal behaviour without relying solely on empirical ageing fits.

How does physics-grounded drug discovery simulation differ from docking software?

Molecular docking software treats the protein and ligand as rigid bodies and scores binding poses by geometry. Physics-grounded simulation uses molecular dynamics to capture conformational flexibility and explicit solvation effects that rigid-body docking misses, predicting binding free energy, ADMET properties, and metabolic activation from first principles. This is particularly valuable for targets where induced-fit binding or solvent-mediated interactions drive selectivity.

What makes GPU-native simulation faster than CPU-based CFD?

GPU parallelism maps naturally to the computational kernels underlying molecular dynamics and CFD: large numbers of independent force or flux calculations that can execute simultaneously. newtsim runs all simulation scales GPU-native, avoiding the CPU-to-GPU data transfers that bottleneck traditional solvers which run physics on CPU and only offload selected operations to the GPU. This removes the transfer overhead and allows the full multiscale chain to run without the latency penalties of heterogeneous pipelines.

Does newtsim replace existing simulation tools like ANSYS or COMSOL?

newtsim fills the multiscale gap, connecting molecular-scale physics to the engineering-scale models that tools like ANSYS operate at. It is not a replacement for FEA-only or single-scale workflows where those tools are the right fit. Where newtsim adds value is in problems where constitutive behaviour, material properties, or interfacial physics need to be derived from lower-scale simulation rather than taken from a handbook or empirical fit.

How long does a fixed-price simulation study take?

Most studies complete in 4 to 8 weeks depending on scope. The standard timeline is: scoping call and problem definition in week 1; simulation execution across the relevant physics scales in weeks 2 to 4; results analysis, validation, and report delivery in weeks 5 to 6. More complex multiscale studies with broader operating envelopes may extend to 8 weeks. Scope and timeline are agreed and fixed before work begins.

What deliverables does a simulation study produce?

A newtsim simulation study delivers: a validated multiscale model calibrated to your system; an operating envelope with physics-based safety margins; a written technical report with findings and actionable recommendations; and optionally CFD visualisations or an ML surrogate model (newtsim Neural) for fast design-space exploration by your engineering team.

Does newtsim work for green hydrogen electrolyser and catalyst design?

Yes. The Stream solver handles proton exchange membrane and alkaline electrolyser CFD: multiphase flow, bubble dynamics, and membrane transport. Bond handles catalyst surface reaction molecular dynamics, giving adsorption energies and reaction barriers from first principles rather than from empirical correlations. Neural then produces fast-to-run surrogate models for design-space exploration and stack optimisation, reducing the iteration cost for new catalyst compositions and electrode architectures.

newtsim is a physics-grounded multiscale simulation consulting firm and software platform. We deliver fixed-price simulation studies and platform licences for industrial clients, covering the full physics chain from quantum mechanics and molecular dynamics through to plant-scale CFD and structural mechanics. All solvers run GPU-native.

Does newtsim use artificial intelligence or machine learning?

Yes. newtsim Neural produces ML-trained surrogate models derived from high-fidelity physics simulations. These surrogates run in milliseconds rather than hours, enabling rapid design-space exploration and real-time control without sacrificing physical accuracy. The ML models are trained on physics simulation data, not empirical measurements, so they inherit the accuracy and extrapolation properties of the underlying physics.

How do I get started with newtsim?

Book a free technical scoping call at newtsim.com/demo. Bring a description of your engineering challenge, the physics you think are important, and any data you have. The call is 45 minutes and produces a problem definition, proposed physics chain, and outline scope that forms the basis of a fixed-price proposal.

Can newtsim simulate non-Newtonian fluids?

Yes. newtsim specialises in non-Newtonian flow: yield-stress fluids, viscoelastic materials, shear-thinning and shear-thickening systems, and complex multiphase flows. The Stream solver handles macroscale CFD for these systems; Bond provides the molecular-scale rheology predictions that replace empirical viscosity fits; and the full chain gives accurate behaviour outside the calibrated range of laboratory data.

Does newtsim work for small companies or startups?

Yes. Fixed-price studies are available to clients of any size. The fixed-price, fixed-scope model means you know the cost before committing, making it accessible for smaller organisations without large simulation teams. Many newtsim clients are startups and scale-ups who need physics-grounded analysis early in product development before investing in laboratory or pilot-scale work.

What is the difference between molecular dynamics and CFD?

Molecular dynamics (MD) simulates the motion of individual atoms and molecules to compute material properties such as viscosity, diffusivity, reaction rates, and phase behaviour, from first principles. Computational fluid dynamics (CFD) models bulk fluid flow at the engineering scale using those material properties as inputs. newtsim connects the two: MD provides accurate constitutive inputs that CFD uses for engineering-scale predictions, rather than relying on empirical correlations.

What is a surrogate model in simulation?

A surrogate model (also called a reduced-order model or emulator) is a fast mathematical approximation of a complex simulation. newtsim Neural builds ML surrogates trained on high-fidelity physics simulation data. Once trained, a surrogate evaluates in milliseconds instead of hours, enabling real-time optimisation and design-space exploration. Because surrogates are trained on physics data, they are more accurate than empirical correlations, especially outside the training range.

What deliverables does a newtsim simulation study produce?

A newtsim simulation study delivers: (1) a validated multiscale model calibrated to your system; (2) an operating envelope with physics-based safety margins; (3) a written technical report with findings and actionable recommendations; and optionally (4) CFD visualisations and an ML surrogate model for fast ongoing design-space exploration by your engineering team.

Resources

EngineeringDecember 25, 202511 min read

Nanoparticle Formulation Stability: Predicting Aggregation, Ostwald Ripening, and Shelf Life from Molecular Physics

Nanoparticle formulations that pass initial QC aggregate on the shelf within months. Accelerated stability testing cannot predict real-world failure modes. Molecular simulation computes the interaction potentials that determine shelf life.

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A pharmaceutical company develops a nanoparticle suspension for injectable drug delivery. Particle size: 120 nm by DLS, polydispersity index 0.08, zeta potential -35 mV. By every standard stability metric, this formulation should be stable. Six months later, the 25C stability sample shows particle growth to 280 nm and visible sedimentation. The 40C accelerated stability sample, paradoxically, looks fine. The Arrhenius extrapolation from accelerated data predicted 24 months of shelf life. The actual shelf life is 6 months.

This scenario (accelerated stability testing that fails to predict real-time behaviour) is common in nanoparticle formulation development. The failure occurs because the dominant instability mechanism at 25C is not the same as at 40C. At elevated temperature, increased Brownian motion and lower viscosity favor rapid aggregation but also faster re-dispersion. At ambient temperature, slow Ostwald ripening (dissolution and regrowth) gradually increases particle size without triggering the aggregation pathway that accelerated testing probes.

The fundamental problem is that accelerated stability testing measures kinetics at the wrong conditions and extrapolates using Arrhenius assumptions that fail when the mechanism changes with temperature. Predicting shelf life requires understanding the molecular-level interactions that drive each instability mechanism, then computing which mechanism dominates at storage conditions.

The Four Instability Mechanisms for Nanoparticle Dispersions

Nanoparticle dispersions can fail through four mechanisms, each with different molecular origins. Aggregation occurs when particles collide and stick due to attractive van der Waals forces overcoming repulsive electrostatic or steric barriers. The aggregation rate depends on the collision frequency (set by Brownian motion and particle concentration) and the sticking probability (set by the height of the interaction energy barrier between particles). Ostwald ripening occurs when smaller particles dissolve and redeposit on larger particles, driven by the higher chemical potential of curved surfaces (Gibbs-Thomson effect). The ripening rate depends on the solubility of the particle material, the interfacial energy, and the diffusion coefficient of dissolved species in the continuous phase.

Coalescence occurs in emulsion-type nanoparticles (lipid nanoparticles, nanoemulsions) when the thin film between two droplets ruptures, merging them into a larger droplet. The coalescence rate depends on film drainage dynamics and the mechanical strength of the interfacial surfactant layer. Sedimentation or creaming occurs when gravity separates the dispersed phase from the continuous phase. The rate depends on particle size, density difference, and continuous phase viscosity. For nanoparticles below 200 nm, Brownian motion typically prevents sedimentation, unless aggregation increases the effective particle size.

Each mechanism has a molecular-level origin, and the dominant mechanism depends on the formulation and storage conditions. Molecular simulation identifies which mechanism limits shelf life by computing the relevant interaction energies and transport properties.

Computing the Particle Interaction Potential

The aggregation stability of a nanoparticle dispersion is determined by the total interaction potential between two approaching particles. Classical DLVO theory computes this as the sum of van der Waals attraction and electrostatic repulsion. But DLVO theory makes assumptions that break down for real nanoparticle formulations: it treats the solvent as a structureless continuum (ignoring solvation forces), assumes the surface charge is uniform (ignoring patchy charge distributions), neglects steric repulsion from adsorbed polymers or surfactants, and ignores hydrophobic interactions (which can be attractive and long-ranged).

Molecular dynamics simulation computes the total interaction potential without these approximations. Build two nanoparticles with their surface coatings (surfactant, polymer, or bare surface with adsorbed ions), place them in a simulation box filled with the continuous phase (water, buffer, or organic solvent), and compute the force between the particles as a function of separation distance. Integrate the force to get the potential of mean force (PMF), the full interaction potential including all molecular-level contributions.

The PMF captures effects that DLVO theory misses. For PEGylated nanoparticles, the simulation shows how PEG chains on opposing surfaces interdigitate at intermediate separation, creating an attractive well that is absent in the simple steric repulsion model. For charged nanoparticles in high-salt media, the simulation reveals ion bridging effects (divalent cations bridging between negatively charged surfaces) that can eliminate the electrostatic barrier entirely. These effects determine whether the formulation is stable or not, and they cannot be predicted from DLVO theory alone.

Predicting Ostwald Ripening Rates

Ostwald ripening is the dominant instability mechanism for many drug nanoparticle formulations. The Lifshitz-Slyozov-Wagner (LSW) theory predicts that the cube of the average particle radius increases linearly with time, with a rate proportional to the solubility of the particle material, the interfacial energy, and the diffusion coefficient in the continuous phase.

The problem is that each of these parameters is difficult to measure experimentally for nanoparticle systems. The solubility of a crystalline drug at 25C may be 1 ug/mL, but the amorphous nanoparticle has a higher apparent solubility (Ostwald-Freundlich effect plus amorphous enhancement) that is hard to measure without dissolution occurring during the measurement. The interfacial energy between a drug crystal and an aqueous surfactant solution depends on the surfactant surface coverage, which varies with particle size and curvature.

Molecular simulation computes these parameters from first principles. The solubility is computed from the free energy of transferring a drug molecule from the nanoparticle interior to the bulk solution. The interfacial energy is computed from the pressure tensor anisotropy at the particle surface. The diffusion coefficient is computed from the mean-square displacement of the drug molecule in the continuous phase. All three parameters are specific to the actual formulation: the actual drug polymorph, the actual surfactant at its actual surface coverage, the actual buffer composition.

With these parameters, the LSW theory (or more sophisticated ripening models that account for polydispersity and concentrated systems) predicts the particle growth rate. For the pharmaceutical nanoparticle example, the simulation predicts that the amorphous drug nanoparticle has a solubility enhancement of 8x over the crystalline form, giving an Ostwald ripening rate of 15 nm3/hour at 25C. Starting from 120 nm, this predicts growth to 280 nm in 6 months, matching the observed instability that accelerated testing at 40°C failed to predict.

Surfactant and Polymer Stabilizer Design

Stabilizing nanoparticle formulations requires selecting the right surfactant or polymer stabilizer. The stabilizer must adsorb strongly to the particle surface (to stay in place during storage), provide sufficient repulsive barrier (electrostatic, steric, or both), and not desorb under dilution (important for injectable formulations that are diluted upon administration). For Ostwald ripening, the stabilizer should also reduce the interfacial energy (slowing ripening) and inhibit crystal growth on existing particles.

Molecular simulation screens stabilizer candidates by computing the adsorption free energy (how strongly the stabilizer binds to the nanoparticle surface), the surface coverage and conformation (whether the stabilizer forms a dense brush or loose mushroom), the contribution to the interaction potential (how much repulsion the stabilizer provides at relevant separation distances), and the interfacial energy reduction (how much the stabilizer lowers the driving force for Ostwald ripening).

For a poorly soluble drug nanoparticle, the simulation might compare Poloxamer 188, HPMC, PVA, TPGS, and SDS as stabilizers. The simulation predicts that Poloxamer 188 provides the strongest steric barrier (PPO block adsorbs, PEO blocks extend into solution) but relatively weak surface binding (risk of desorption under dilution). HPMC provides moderate steric repulsion but strong surface adhesion through multiple hydrogen bonds. TPGS provides moderate steric repulsion plus the lowest interfacial energy (reducing Ostwald ripening by 60%). The optimal stabilizer choice depends on which instability mechanism dominates, which the simulation provides.

Shelf Life Prediction Without Accelerated Testing

The ultimate goal is predicting shelf life from formulation composition without waiting for real-time stability data. The simulation-based approach works as follows: compute the interaction potential between particles to assess aggregation stability, compute the Ostwald ripening rate from solubility, interfacial energy, and diffusion coefficient, compute the sedimentation rate from particle size and density, and identify the rate-limiting instability mechanism at storage temperature.

The shelf life is determined by the fastest mechanism, the one that first drives the product out of specification. For each mechanism, the simulation computes the rate and extrapolates to the time at which the specification limit (particle size, PDI, drug content, appearance) is exceeded.

This approach does not replace real-time stability testing for regulatory filing. ICH guidelines require real-time data. But it does replace accelerated stability testing as a predictive tool during formulation development, when you need to make decisions months before real-time data is available. Instead of waiting 6 months to learn that a formulation fails, the simulation predicts it in days, allowing rapid iteration to a stable formulation.

Formulation Optimization Workflow

The simulation-guided formulation workflow for nanoparticle systems involves defining the target product profile (particle size, drug loading, stability requirement), screening stabilizer candidates computationally (5-20 candidates, 1-2 days), predicting the dominant instability mechanism for each candidate, optimizing stabilizer concentration and co-stabilizer combinations, and confirming the top 3-5 formulations with real-time stability testing.

The computational screening eliminates 80-90% of candidates before any lab work begins. The remaining candidates are the ones with the highest predicted stability, not the ones that happened to be tested first in a trial-and-error campaign.

Economics of simulation-guided nanoparticle formulation:

Traditional screening (20 stabilizers x 5 concentrations): $150K-400K, 6-12 months
Simulation-guided screening (100 virtual + 5 physical): $30K-80K, 6-8 weeks
Failed stability at 6 months (traditional): $200K+ sunk cost, restart development
Simulation predicts failure in days: iterate before committing to stability campaigns
Time-to-stable formulation: 3-6 months faster

For a pharmaceutical company with 3-5 nanoparticle programs in development, simulation-guided formulation saves $500K-2M annually in direct costs and 6-12 months in development timelines. The indirect value (avoiding late-stage formulation failures that delay clinical programs) can be worth $10M+ per program. Explore MolSim for nanoparticle and formulation simulation, or discuss your nanoparticle formulation challenges with our team.