If we think about the major challenges facing engineering in the next few decades, virtually every one comes down to questions about materials. From an environmental perspective we’re facing resource shortages in common materials, such as oil, lithium, and cobalt, as well as rare metals, like platinum, that are essential components of computer hard drives and wind turbines. Moreover, when it comes to infrastructure, corrosion and a lack of maintenance have left many important structures at risk of collapse. And, if we are going to make the shift towards renewable energy sources, continuing improvements are needed in materials that facilitate energy capturing technology like solar panels and batteries.
Unfortunately, many of these challenges are not going to be solved with the materials we have today. For example, although concerns around waste plastic demand an alternative material, we can’t simply stop using plastic by replacing it with paper and cardboard – which come with their own issues. These include overzealous deforestation, and the amount of water required for paper – for instance, making a paper bag requires 17 times more water than a plastic bag. Therefore, we need to look towards new materials and innovative solutions, like wood sponges and Silicon X, in order to find a way through these conundrums.
Materials innovation in the digital era
However, despite such discoveries, many companies are finding it difficult to make substantial research breakthroughs when it comes to material innovation. This is, counterintuitively, because researchers are dealing with too much information. Although it might seem odd to complain about having too much data, researchers are drowning in the flood of journal articles, conference papers, internal experiments from R&D, and third-party data sources. On top of this, the more that companies invest in technology and hardware, and automate processes within the lab and research settings, the more data are generated.
Today, chemical and materials scientists have access to data generated by everything from smart lab equipment to connected lab sensors. But much of this data is currently going unanalyzed, for the simple reason that there isn’t enough time to review it all – it is estimated that it would take a lifetime to analyze the data generated by just one sensor on a manufacturing assembly line. This is coupled with the fact that data are arriving into the workflows of scientists in many different formats; they can spend 80% of their time on tasks such as cleansing, integrating, and formatting data to make it usable. The menial nature of these tasks, combined with the ever-growing data scientist shortage, can make it difficult for companies to hold on to talent as experienced employees leave to try and find more exciting roles. Even when companies do manage to retain their best people, the huge volumes of potentially relevant information still mean that researchers can’t be sure they’ve found everything they need.
Achieving true digital transformation
To deal with the informational overload, materials and chemicals organizations and labs are investing in digital transformation capabilities. The idea is that technology tools can help capture and analyze vast amounts of data, assisting researchers in pulling out the most important insights. However, digitization and advances in technology have proved extremely efficient at generating and storing information, but many organizations are finding the analytical stage more troublesome. Digital transformation is often thought of as solely capturing and centralizing data, but doing this doesn’t necessarily mean you can do anything with it – implementing a centralized data repository is only useful if companies also invest in the infrastructure to make it accessible and valuable. For example, a sensor in a lab might generate lots of data about material density or malleability but translating that into performance under specific conditions has proved more elusive.
This is because complete digital transformation involves not only generating, capturing and storing data but also being able to retrieve it so that researchers can access, visualize and collaborate effectively. Each of these come with a host of issues that need to be overcome. For example, in a recent article, Veslava Osinska outlined a number of key difficulties scientists face when trying to visualize complex data, including referencing scientific theories, resolving topological representation issues, and how to best render the data so that it is interpretable. The issue for materials scientists is these problems are extremely difficult to address when data is siloed in different places and formats across the organization and its external partners.
Digital problems need digital solutions
Solving this dilemma is a complex challenge and many chemicals and materials companies have started to address the issue by investing in digital infrastructure and big data technologies, for example, data lakes. These are positive first steps, but the next stage is for organizations to embrace a data-centric, platform IT approach where data can move freely between applications. This is a single, agile layer, on which all applications can sit and be searched from one interface, and data transferred between applications seamlessly – whether an Electronic Lab Notebook (ELN), a chemical-sourcing database, inventory management tool or Lab Information Management System (LIMS) – and it is the ultimate goal of the digital transformation journey.
The transition isn’t always easy or quick, but the benefits are tremendous. For example, if a researcher wanted to understand the performance of an inorganic catalyst, they would need to know the compositional data as well as processing data. In many enterprises, such data are stored in different silos but in a platform environment, both would be available in any application, allowing them to focus on analysis rather than hunting for the right bit of data. This approach means all relevant data are available, searchable, and usable in a seamless end-to-end workflow, increasing the efficiency and productivity of researchers by making it easier to visualize data through connected applications.
Meeting the needs of the future
The growing environmental crisis poses unpredictable threats to life on Earth. However, for chemical and material scientists, it also represents an opportunity. The need for new materials will never be greater, and materials innovation will require the input of a multitude of disciplines, including synthetic chemistry, polymer science, formulation and process development. As a result, scientists, chemists, and engineers will require an inclusive view of all research data associated with any given project, as opposed to a siloed view in which each discipline deals with their data in separate IT environments. Right now, scientists are inundated with more information than they can hope to process, and the priority for companies should be providing researchers with the digital tools that help to manage and control data, while increasing the rate of innovation in our time of need.
Written by: Max Petersen, AVP, Chemicals & Materials, Dotmatics