• Insights

Published on August 1, 2022 by Angela Blake

Authors Nick Tito and Angela Blake

One of the core philosophies on which RheoCube is based, is to develop it dynamically alongside industry. We acknowledge the deep value of input we receive from industry – real life experiences and challenges in R&D are what matter. As part of that connection to industry, we constantly learn from the real world, and some interesting insights have arisen along the way. Formulations are a key part of discussions. Two key challenges that come up in conversation with potential and current clients, which we see as actual pain points, are statements such as:

“We want to include all the ingredients in the formulation in our simulations, otherwise digital R&D doesn’t work for us,”


“A solution like RheoCube is too complex, it will scare my formulators off. I’ll give it to a few experts in our company to use, who are more interested in the theoretical or fundamental side of science.”

Let’s kick this one off with: “The whys?”

Why should you not include the full formulation?

Why should RheoCube investigate mixtures of a few components, and not a full formulation like what is done at the laboratory scale?…

It’s our belief that industry may be approaching Digital R&D from the perspective of traditional lab processes. In a lab formulation, one mixes together their twenty (or more) ingredients, and sees what comes out. It’s a combination of trial-and-error, experienced/educated guessing, word-of-mouth, and rule-of-thumb. It certainly works, and after enough experimentation one is often able to arrive at a viable recipe for their formulation.

However, this approach is remarkably inefficient in more ways than one. It’s wastefully costly, as the vast majority of person-hours and ingredients are invested into failed directions. This approach also fails to yield transferable knowledge for new R&D projects, as it’s typically not clear why the successful formulation is so good, nor why the failed formulations are poor. In a world of fast-changing consumer demands and huge pressure to re-formulate from the ground up, the traditional route will not be enough to serve tomorrow’s market. New techniques and a new mindset are needed.

It’s time to look at fluids on a far more detailed scale

What’s needed is a simulation platform that looks at the molecular properties on the nano-scale, how those interactions bridge up to the meso-scale, and then how those interactions give rise to droplets, emulsion properties and colloidal suspensions. Multiscale simulation enables R&D to happen via this bottom-up approach, all the way from handfuls of molecules to the whole mixture. A formulation can be built “ingredient by ingredient”, starting from simple and building to high complexity. At each stage, the simulations afford detailed insight into what works, and why. This is powerful transferable knowledge within your R&D group.

“ALL simulations are simplified, and they all give valuable output. Scientists like me are just not used to simplifying as a mindset.” ~ Merit van der Lee, Science Team Lead at RheoCube

This is the challenge that we in RheoCube take on, with an eye towards helping practical R&D groups embrace this powerful realm of science. RheoCube enables full multiscale simulations to be launched in the cloud right from within a friendly web-based UI. Large swaths of ingredient combinations that normally have to wait in a queue for laboratory instrumentation can be launched in parallel in the cloud.

How does the simulation capture all the interactions and processes in a formulation?

Simply put, it can’t. Simulation is a bit like a tool in the laboratory: it needs an expert to first build the instrument, so that it’s designed to respond to the most important observables of a sample. But unlike an instrument, which is normally designed to measure just one or two observables, simulation is multi-purpose. At RheoCube, our in-house modeling experts do all the heavy lifting of identifying the latest and greatest methods in theoretical chemistry and physics, and putting them into our simulation platform. Out of the box, this means that RheoCube is built to capture the most important physics happening at the nano- and meso-scale in a formulation. If there’s a specific set of chemistry and physics needed to capture your class of formulations, then we have the knowledge to build it in for you. Some smart choices have to be made so we approach it like a prototyping stage, basing it on approximations on what we do currently know. 

“We simply can’t capture, as a simulation community, the entirety of physics and chemistry happening all the way from molecules to mixtures in one go. With RheoCube, we try to determine the most important parts of the physics at the molecular and the meso-scale that we believe, as experts in the field, will drive the most important rheological properties of that material.” ~ Nicholas Tito, Modeling Expert at RheoCube

How do we know these rheological properties?

It’s our expertise – we have a team of several modelers and scientific consultants – encompassing over 40 years in top-academic research. We designed RheoCube harnessing all of this knowledge on what kind of physical principles govern rheology in complex fluids.

What are complex fluids?

Any kind of fluid where the molecular properties and molecular interactions lead to emergent properties of the fluid on our scale of existence that are interesting, or functional, or remarkable in some way (e.g. water is a complex fluid as it’s not fully understood, if at all). For the formulations industry, a complex fluid is any fluid that has at least two ingredients that are mixed, that have some interplay going on between them, and that is giving rise to functionality that we as humans appreciate in some way.

With all this upfront choice-making, we now start running into the issue of trust.

How do we trust this?

Validations. This is where we marry theory, computing and real life lab results. We cannot validate everything,  but we certainly can validate the basics. The interactions we’ve chosen to include are roughly correct within a given range, we then have to make an assumption as scientists on which ingredients to use, where the ingredients have similar properties, thus choosing to include them in the interactions (e.g. surfactants, non-ionic surfactants, etc.)

So, what’s the innovative value relative to traditional wet lab research?

What is the value that can then be derived from a simulation model such as RheoCube – knowing that choices have to be made along the way? How can RheoCube create value out of a simulation model as opposed to what you would do in a wet lab?

Let’s explore this by looking at surfactants: 

Surfactant molecules live on the nanoscale, we cannot see them and don’t know what is happening there. For this reason, there are very massive and complex experimental studies underway running into the millions of Euros to perform, to see what kind of structures are formed in surfactants. But in order to conduct these investigations, x-ray and neutron scatterings need to be carried out and so visits to reactor institutes are required (such as in Delft, the Netherlands). This entails securing and paying for a slot at the reactor institute, attending with a group of people in order to be able to record 24-hour measurements, and then analyzing the collected data over the course of the next few months. All this just to get an idea of a complex fluid on the nano scale.

Why then would we trust a simulation over such a detailed experimental measurement like this?

X-ray scattering is a proxy for what is really happening at the nano-scale, just like other instruments use different approaches to measure quantities of interest in the material. All of these instruments have strengths, weaknesses, and sources of error and uncertainty. Simulation can be viewed in much the same way: it is a tool, providing measurements to the material, and also has its own sources of uncertainty and error. 

Simulation brings you into uncharted territory at the click of a button.

Adhesives have a certain viscosity and might or might not be dependent on the size and shape distribution of the particles therein. In the lab, you could arduously work out the sensitivity of these variables by designing various distributions of particles of different shapes and sizes, putting them into the formulation, and then carrying out the rheological measurements, and finally you’d approximate it. But this is impractical as:

  • firstly, it’s impossible to carefully design these particles with a specific shape and size distribution, and
  • secondly, you’re getting your particles from a certain supplier who only supplies a certain range of particles.

  • With a RheoCube simulation, however, you can conduct this experiment with a click of a button, specifying the particle properties as you want them, and you can launch and run several simulations simultaneously. RheoCube also has the tools to help you analyze the data and determine the final level of specificity of the particles in order to meet that certain viscosity, which you can then inform your supplier of. 

    This level of sensitivity is increasingly important as we push the boundary of using these materials in more and more demanding applications. For example, we don’t want the adhesive to decompose or lose its viscosity because the temperature increases by just a few degrees (which we’ve seen happen in a real life industry case). As an R&D company, you need to have a handle on the tolerance of your design before you upscale it to production and then push it out to market, or risk having to go through recalls and fixing the product – a very costly exercise for any business to have to undertake. 

    Augmenting the laboratory with a virtual prototyping world:

    In the lab, a typical trial-and-error experimental formulations process generally entails: 

  • delving into the cabinet and selecting various ingredients to experiment with (whatever is at one’s disposal)
  • mostly (if not solely) basing the parameters of the experiment on the knowledge base of an expert who has years of experience in conducting these types of experiments, and with that a certain understanding of how certain ingredients work and interact with one another
  • being limited in only using ingredients the supplier sends, or worse yet, only using what is known
  • We’re just not sure how smart this scenario is – for us it’s starting to look reactive as opposed to proactive, and perhaps limiting.

    RheoCube recognises that it’s time to flip this scenario on its head! On a daily basis we speak with innovative industry leading companies, seeking a way to work smarter by learning upfront what is needed to get the result actually wanted. Smarter formulations by simulations. 

    Simulations allow one to be much more targeted and explore a far wider range of ingredients.There will still be question marks, and the actual wet lab experiment will still need to take place. However, conducting the simulation from the outset will provide far more meaningful results to act as a sound compass in the experimental journey. 

    Traditionally, the mantra in R&D has been: trust experiments and reinforce those findings with data. With the emergence of new technologies however, (e.g. simulations, friendly UI, high performance computing) this mantra has changed to: trust data and reinforce that with experiments. 

    Simulations can create a lot of value for any formulation process.

    Today there are so many variables that need to be factored in, from fluctuating market demands, pricing, compliance around environmental-friendliness and the use of non-fossil fuels, meeting product health and safety standards, and so on. The experimental formulation environment has become a far more complex one to navigate in. And because RheoCube functions within this environment, there will still be a lot of choices for RheoCube to make as a simulation model, along with a lot of ruling out.

    What then IS the benefit to simplifying? 

    So, coming full circle back to the initial question posed at the beginning of this article: Why is a drive to see the full formulation in simulations actually missing the point? It comes down to practicality. We could do quantum simulations for a whole system but it would take a lifetime to carry out when done at the level of a formulation. The way forward is to simplify. That’s what will deliver better insights. You can hone into what core physical processes are leading to the functionality of your material. You can do an approximation in the simulation model with the physics as well as the ingredients, using reference states (for example, ingredients that have the same concentration of salt).

    In a simulation you can assume that certain formulations or categories of formulations are all based on the same basic reference state, which simplifies the formulation simulation, making the simulation happen in a reasonable time frame to get a result that’s relevant for your work today (and not in two months time), and also helps you isolate which physical processes at the micro and nano scale are leading to the functionality of your material.

    Thus, the benefit to simplifying, and therefore RheoCube as a Simulation-as-a-Service solution, is also in the time and cost factor benefits. And aren’t these ultimately the two fundamental concerns driving any industry and business?