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

EngineeringJanuary 8, 202611 min read

Polymer Processing and Crystallization: Predicting Microstructure from Molecular Simulation Instead of Trial Runs

Polymer part properties depend on crystallization kinetics during cooling, and those kinetics depend on molecular weight, branching, and processing conditions. Lab trials cannot explore this parameter space fast enough.

newtsim engineering

An injection molder receives a new lot of polypropylene from the resin supplier. Same grade number, same datasheet properties (MFI 12 g/10min, density 0.905 g/cm3, tensile strength 34 MPa). The operator loads the resin and runs the same process parameters as the previous lot. The parts come out with visible flow marks, 8% higher warpage, and the impact strength is 20% below spec. Nothing on the datasheet changed, but the parts are unacceptable.

The root cause is molecular microstructure variation that the datasheet does not capture. The new lot has a slightly broader molecular weight distribution (Mw/Mn of 5.2 vs 4.6 in the previous lot) and a different tacticity distribution in the high-molecular-weight tail. These differences do not show up in the MFI measurement (which averages over all molecular weights at a single shear rate) but profoundly affect crystallization kinetics during cooling in the mold, which determines the final part microstructure, mechanical properties, and dimensional stability.

This problem is pervasive in polymer processing. Part properties are determined not by the resin's bulk properties but by the crystalline microstructure that forms during processing. And crystalline microstructure depends on cooling rate, flow-induced orientation, molecular weight distribution, branching, and nucleating agent concentration, a parameter space so large that empirical optimization by trial runs cannot cover it.

How Crystallization Determines Part Properties

Semi-crystalline polymers (polyethylene, polypropylene, nylon, PET) solidify by forming crystalline lamellae (thin ordered sheets of folded polymer chains) embedded in an amorphous matrix. The degree of crystallinity, the size and perfection of the lamellae, and the orientation of the crystalline phase relative to the flow direction all affect mechanical properties.

Higher crystallinity increases stiffness and chemical resistance but decreases toughness and transparency. Oriented crystallization (shish-kebab structures formed under flow) dramatically increases tensile strength in the flow direction but creates anisotropy. Nucleation density controls spherulite size, with more nuclei producing smaller spherulites that generally improve toughness and optical clarity. And the crystallization rate relative to the cooling rate determines whether the polymer achieves its equilibrium crystallinity or is frozen in a partially amorphous state.

All of these outcomes depend on molecular-level events: chain folding into lamellae, nucleation of crystalline order from the melt, chain transport to the growth front, and the competition between different crystal polymorphs (alpha, beta, and gamma forms in polypropylene, each with different properties). The processing conditions (melt temperature, mold temperature, injection speed, packing pressure, cooling rate) set the boundary conditions, but the molecular-level kinetics determine the outcome.

Why Datasheet Properties Are Insufficient

Polymer datasheets report properties measured under standard conditions (ISO or ASTM test specimens, specific molding conditions). These properties are useful for grade selection but inadequate for predicting part performance because they represent one point in a vast processing-property space.

Melt flow index (MFI) measures viscosity at one shear rate (typically 2.16 kg load) and one temperature (230C for PP). Real processing involves shear rates from 100 to 100,000 s⁻¹, and viscosity at high shear rates (which determines filling behavior) is not predictable from MFI alone for polymers with different molecular weight distributions. Two resins with identical MFI can have viscosities that differ by 3x at 10,000 s⁻¹ because of their different molecular weight distributions.

Crystallization behavior is not reported on datasheets at all. DSC crystallization temperature and enthalpy are sometimes available but represent quiescent crystallization at 10-20C/min. In an injection mold, the polymer experiences cooling rates of 50-500C/s near the wall and simultaneous shear flow that accelerates crystallization by 10-100x through flow-induced nucleation. The datasheet tells you nothing about how the resin crystallizes under these conditions.

Molecular Simulation of Crystallization Kinetics

Molecular dynamics simulation predicts crystallization behavior from the molecular structure of the polymer. The simulation starts with an equilibrated polymer melt (50-100 chains of defined molecular weight and branching) at processing temperature. The system is then cooled at a controlled rate while monitoring the development of crystalline order.

The simulation directly captures the nucleation rate as a function of undercooling and molecular weight. Longer chains nucleate slower (higher entropic barrier to forming an ordered fold) but produce more stable crystals. Branched chains nucleate even slower: short-chain branches (SCBs) in LLDPE are excluded from the crystal lattice and must be accommodated at the fold surface, which costs energy. The simulation quantifies these effects for specific molecular architectures, not generic polymer types.

Flow-induced crystallization is particularly important for injection molding and fiber spinning. Our simulation approach applies a controlled shear or extensional flow to the melt and observes how the stretched chain conformations accelerate nucleation. Above a critical shear rate (which depends on the longest relaxation time of the polymer), extended chain segments serve as row nuclei for the formation of shish-kebab crystalline morphology. The simulation predicts both the critical shear rate and the resulting morphology: oriented crystalline layers near the wall (high shear) transitioning to spherulitic core (low shear) in the centre of the part.

Predicting Lot-to-Lot Variation

Returning to the polypropylene injection molding problem: molecular simulation explains and predicts the lot-to-lot variation that datasheets miss. Given the GPC (gel permeation chromatography) curve for each lot (which measures the full molecular weight distribution), the simulation predicts the crystallization kinetics, the resulting microstructure, and ultimately the part properties for each lot.

The broader MWD of the problematic lot means a larger fraction of very high molecular weight chains. These chains have longer relaxation times, which means they orient more readily under flow (more shish-kebab formation near the wall) and crystallize differently in the core. The simulation predicts that this lot will produce parts with a thicker oriented skin layer (causing the flow marks), higher anisotropic shrinkage (causing the warpage), and lower impact strength (because the thicker skin layer reduces the energy-absorbing amorphous content).

The simulation also identifies the process adjustments that compensate for the lot variation. Increasing mold temperature by 10C reduces the skin-core difference and suppresses flow marks. Reducing injection speed lowers the wall shear rate below the critical level for oriented crystallization. Extending the packing phase improves dimensional stability. These adjustments can be computed and applied before the first shot, eliminating the hours of trial-and-error that typically follow a lot change.

Nucleating Agent Design

Nucleating agents are additives that increase the nucleation density and crystallization temperature of semi-crystalline polymers. They are widely used to improve optical clarity (smaller spherulites scatter less light), increase stiffness (higher crystallinity from faster crystallization), and reduce cycle time (faster crystallization means shorter cooling time in the mold). The global market for polymer nucleating agents is approximately $300M/year.

Nucleating agent effectiveness depends on epitaxial matching between the agent's crystal surface and the polymer crystal lattice. If the lattice spacings match within 5-10%, the polymer nucleates heterogeneously on the agent surface at lower undercooling than homogeneous nucleation. Molecular simulation computes the epitaxial match by placing polymer chains on the nucleating agent surface and computing the interfacial energy. Low interfacial energy means good epitaxial matching and effective nucleation.

This enables computational screening of nucleating agent candidates. For polypropylene, the simulation evaluates the effectiveness of different crystal structures (sodium benzoate, dibenzylidene sorbitol, calcium stearate, talc) and predicts the optimal particle size and concentration for each. The screening identifies candidates that promote the desired crystal polymorph (alpha for clarity, beta for toughness) and achieves the target nucleation density.

Integration with Process Simulation

Molecular simulation of crystallization kinetics integrates into injection molding simulation (Moldflow, Moldex3D, or equivalent) as a material model that replaces the empirical crystallization kinetics typically used. Instead of fitting the Nakamura or Hoffman-Lauritzen parameters to DSC data, the process simulation uses crystallization kinetics computed from the actual molecular structure of the resin being processed.

This enables the process simulation to predict part properties (not just filling and cooling patterns) from resin molecular structure and processing conditions, the effect of lot-to-lot variation on part quality, optimal processing conditions for a new resin grade without trial molding, and the effect of regrind addition (which changes the MWD) on crystallization and part properties.

Economics of simulation-guided polymer processing:

Trial run cost per new resin/mold setup: $5K-25K (material, machine time, testing)
Average trial runs per setup: 3-8 iterations to achieve spec
Simulation reduces to 1-2 confirmation runs: saves $10K-150K per setup
Cycle time reduction from optimized crystallization: 5-15% (worth $50K-500K/year per mold)
Scrap reduction from predicted lot variation: 2-5% (worth $100K-1M/year for high-volume parts)

For a large polymer processor running 50+ molds with frequent resin and lot changes, simulation-guided processing saves $1M-5M annually in reduced trial runs, faster cycle times, and lower scrap rates. The competitive advantage is consistency: delivering in-spec parts from the first shot, regardless of resin lot variation. Explore MolSim for polymer and materials simulation, or discuss your polymer processing challenges with our team.