Sean McGee
Product Marketing Manager, BIOVIA

Today’s engineers are in a bind: increasingly advanced product requirements are exceeding the capabilities of the materials that are immediately at hand. Electrical components need to be smaller; plastics require more varied and sustainable feedstocks; buildings must use less energy; and physicians want to use medical devices tailored on a patient-by-patient basis. As a result, engineers are increasingly thinking outside the box. For example, the Boeing 787 Dreamliner is constructed of 50 percent composite materials by weight, helping to improve its fuel efficiency by 20 percent over previous models. The value these advanced materials bring to engineers is clear, but quickly screening new materials remains a challenge.

This challenge extends beyond what material or combination of materials an engineer could use; the structures of the materials or how the finished part is manufactured can impact the final design. To truly take advantage of the benefits offered by advanced materials, engineers need to adopt a “multiscale” approach to materials design, considering how different length scales (i.e., from macroscopic to microscopic to quantum) and diverse structures contribute to the bulk properties of their final product. Doing so can open the door to faster, more iterative and more widespread adoption of cutting-edge technologies and design methodologies, such as nanotechnology, biomimicry and “smart” materials.

Materials properties are inherently multiscale. Simply speaking, characteristics established at the quantum level add up across atoms, molecules and lattices (or lack thereof) to produce the properties we see at the bulk level. These nested hierarchies of nano-, micro-, meso- and macro-scales are fundamentally tied, and variations at any level can have an impact across the rest of this length spectrum. Science has allowed researchers to explore how these attributes change across these length scales; nanotechnology research is based upon how classically observed properties of certain materials vary as we approach the atomic scale. These properties extend beyond length scales as well. For example, scientists have recently theorized that under the appropriate conditions, such as extreme pressure, non-metals such as hydrogen begin to adopt metallic properties. Developing an understanding of how various materials properties develop over differing length scales and their surrounding environment, then, provides the first step toward achieving multiscale thinking.

However, there is no single formula that can describe these changes as we move from the subatomic to macroscale. It requires building bridges between multiple fields of physics and accounting for the additive effects of small changes in material purity and structure. Achieving this goal means moving beyond physical experimentation to utilize advances in materials modeling and simulation. These tools allow scientists to quickly run and iterate experiments in silico to more rapidly screen different combinations of materials and structures to optimize the final product’s performance.

Adopting modeling and simulation tools to augment product design presents a unique opportunity for engineers: instead of designing a part based on the restrictions of what materials are available, they can design a material based on what materials properties their part needs. This would allow teams to design more freely, allowing them to quickly make changes to products based on variations in cost, supply, regulatory requirements or customer demands. They can also test more “moon shot” ideas in a low-risk environment, potentially uncovering discoveries that could accelerate new product development. As the amount of data available to researchers grows, utilizing advances in artificial intelligence and machine learning to uncover trends in existing research and predict the potential success of future work would allow scientists and engineers to proactively guide their research with data-driven decisions. Therefore, the benefits of multiscale materials modeling and simulation extend beyond pure business or scientific value; they comprehensively advance R&D productivity. This allows scientists to actively explore new areas of cutting-edge research and materials methodologies, including:

•    Nanomaterials – Designing materials at the nanometer scale unlocks unique properties unavailable to bulk materials. Scientists can isolate these “quantum-derived” properties by leveraging the additive properties of the mesoscale. For example, graphene, a repeating network of carbon molecules arranged in one-atom-thick sheets, possesses unique electrical properties making it ideal for batteries. At the same time, graphene is one of the strongest materials known. Multiscale modeling enables scientists to explore the unique properties of nanomaterials, quickly and leanly identifying various formulations in silico to design materials with bespoke properties for each new application.
•    Biomimicry – Nature has spent the past 4 billion years iteratively tweaking combinations of elements and structures to create materials with properties that rival and surpass the best we can make today. Consider bone, abalone shells and spider silk; they have been optimized for lightweighting, fracture resistance and strength, traits that are desired in a myriad of practical applications such as building materials, transportation and medical prosthetics. Amazingly, these materials can also be self-assembled at room temperature in aqueous solutions with little to no waste. Multiscale modeling allows scientists to understand the molecular interactions and gradients of these materials, even to mimic the “green” manufacturing processes they utilize at scale.
•    Smart materials – While nature has engineered finely tuned materials, it has also “programmed” systems of materials that react to their environment. The shape of a piece of wood will change with temperature, pH, gravity or electric or magnetic fields. These properties can be “tuned” to provide optimal functions across a variety of environments. Additionally, wood can heal itself given time and basic inputs like water and CO2. Multiscale modeling provides a unique window into this world of materials “systems,” often illuminating the fundamental processes that make them up.

The main challenge to adopting these sorts of materials, however, centers on developing scalable manufacturing processes for them, connecting the virtual world to the real world. In the case of biomimicry, for example, engineers would need to recreate the microscopic manufacturing components of individual cells or tissues. Current technological limitations do place some roadblocks in the way. However, new advances in the speed and precision of additive manufacturing techniques may provide a way forward. Additive manufacturing provides clear benefits over the traditional reductive methods of “subtractive” manufacturing—cutting away material from a solid block to produce the shape of a new part—as it produces less waste and allows designers to incorporate internal structures into the design of the part. At its core, additive manufacturing mimics the layer-based deposition of materials seen in many natural processes, producing hierarchical structures with gradients of properties and materials. While older additive techniques relied on the sequential curing of photoreactive resins or the deposition of hot plastics to build a part, advances in this space are unlocking new materials and methods that are catalyzing change. Constituent layers are becoming thinner, machine resolution and precision are improving, and build speed is increasing. Machines can alternate the materials they print with, to the point where parts can be designed with gradient materials and properties in mind. All of these factors combine to provide scientists and engineers with the tools they need to take their first steps toward true multiscale thinking.

As organizations progress toward more widespread adoption of virtual tools to augment materials design, it becomes increasingly important to stress the value of real-world testing. Models and simulations offer tools to weed out low-quality materials and structures for a final part, but the insights they provide depend entirely upon the quality of the data underlying them. As a result, in silico and real-world testing should not be used exclusively. Instead, a harmonization of these approaches provides the greatest value: experiments provide the data required to build a compelling model, while a model directs what should be tested next, providing the foundation to refine the original model or develop a new one. This feedback loop allows R&D teams to make the most of their existing knowledge resources and better allocate their efforts for future work, opening the door to multiscale thinking and shortening the time to innovation. Doing so can release many of today’s researchers from the bind they find themselves in.

Key properties of multiwall carbon nanotubes give rise to a number of existing and potential applications, including conductive polymers and composites conductive thin film coatings, petrochemical catalysts, advanced ceramics and lithium ion batteries.