top of page

An Operating System Approach to Advanced Materials Commercialization

The path from an idea to market is rarely an easy one, particularly to make the leap from a piece of scientific research to a commercialized technology. Creating a high-value solution from a promising material sometimes feels like alchemy. But it does not have to be that way. By going back to workflow fundamentals – taking an operating system approach, if you will – it is possible to dramatically cut back the commercialization cycle to solve persistent materials innovation challenges.

Materials Challenges

Not all commercialization gaps between science and technology are equal. According to research by McKinsey and Company, it can take as many as 20 years between the time a novel material is developed and when the products and processes it enables  yield substantial revenue – putting it well past other industries such as pharmaceuticals or aeronautics.

Part of this gap is due to fundamental limits in research and development resources and the ingenuity of the human mind. The other challenge, however, is scale: While novel materials have fundamental properties that may be compelling at small scale, they are often divorced from end-product or process performance once integrated into a full-scale system.

While many specialty chemical companies aspire to participate in the full value chain – from material discovery to end-product – few have successfully bridged this capability gap. As a result, new products and processes often only realize incremental improvements and line extensions, limited by the performance characteristics of existing materials.

At NuMat, we are creating a new way forward with a few operating-system level principles:

  1. Design with purpose

  2. Leverage high-performance computing and high-throughput experimentation

  3. Develop an end-to-end solution

These principles are creating a new workflow for molecular engineering that is applicable across industries.

Designing with Purpose

Traditionally, new materials have been independently discovered and synthesized by chemical companies or academic labs, and then a technology company will purchase available samples to test them for a product or process of interest. That does not always translate well.

For the materials we work with at NuMat, a group of nanoporous crystals called metal organic frameworks, or MOFs, there are a near-infinite number of possible structures. Testing a material that is not tailor designed for a specific use case will typically not work in a fully engineered system. An off-the-shelf MOF, however well marketed, will invariably fail to achieve system-level performance requirements, leading to market misperception of material class viability.

In NuMat’s new integrated model, we design the material for the system, versus designing the system around the material. Computational scientists, chemists, and engineers work collaboratively with our partners to define market requirements for success and then work backwards to design, validate, scale, and integrate tailor-designed materials into novel products and processes.

Leveraging Computation

NuMat is leveraging the power of supercomputing to discover and select the right material for the right application. Computationally-driven high-throughput screening can take the infinite world of MOFs and bring it down to only the most promising candidates to be validated in the lab. This requires sophisticated algorithms that can run fast, accurate, high-powered simulations of millions of structures at a time.

As cloud computing has become more affordable over the last several years, the process of screening potential materials has become significantly cheaper and faster. Whereas 10 years ago, it may have cost tens of thousands of dollars and a full team of computational scientists months to run limited simulations, complex simulations on millions of structures now occur in just days and for a fraction of the cost. Computational cost will only continue to decrease with emerging technologies such as quantum computing.

Developing an End-to-End Solution

While computational tools are a powerful R&D accelerator, material design is ultimately an iterative process that requires experimental validation and a rapid feedback loop. Materials science technology must bring together computation with chemistry and systems engineering to develop an end-to-end solution.

At NuMat, for example, all synthesized samples undergo rigorous high-throughput analytical and characterization testing to match predicted performance with experimental data. The computational team then refines the model as needed to further improve the material selection process. This rapid feedback loop enables us to quickly hone in on a set of targeted structures for scale-up, while also creating a wealth of proprietary data that can be leveraged across diverse product platforms.

The scaling process also requires significant prototype testing and systems engineering. Application and design engineers must integrate the materials into purpose-built, pilot-scale prototype systems for validation. Based on system-level performance data, the design of a material may be adjusted, or the team may go back to earlier stages in the material selection process to meet the performance requirements. This feedback-driven process is crucial to the workflow and helps enable a smooth transition to market in tight partnership with customers.

Making a Materials Shift

The marriage of materials and systems design is leading to a meaningful leap forward the ability to commercialize innovative materials science technology. The workflow we have created at NuMat is replicable and transferable in filling the broader commercializing gap between materials discovery and end-product engineering. Through this operating systems approach, we are creating new high-value markets across a variety of sectors, from electronics and life sciences to industrial and defense.

This shift follows a larger global trend in design and innovation – one in which companies are increasingly creating new workflows that are less reliant on outside suppliers so that they can better customize and innovate. Think of how Apple is now creating its own chips, how Google is buying underwater Internet cables, or how Tesla has created its own auto parts. That’s how we see the future of materials innovation.

bottom of page