Key Challenges in Research and Development of Chemicals and Materials

The chemical and materials (C&M) industry contributes roughly $5.7 trillion to global GDP and supports an estimated 120 million jobs, according to a recent report published by the International Council of Chemical Associations (ICCA). To meet massive demand, chemicals and materials research and development (R&D) teams face many challenges and top amongst them is speed.

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These teams need to accelerate how they conduct their research, get to a material, and get to market. That means adopting nimble workflows and flexible informatics solutions that let them:

React quickly

Customers often demand incredibly short turnaround times of weeks or even days. On-time delivery can be complicated by secondary challenges, such as regulatory changes that restrict usage or access of key ingredients, or supply chain issues due to external factors, such as COVID-19, conflict, weather events, trade embargos, and taxes. Careful accounting of exactly when and how ingredients are used is essential so that finding suitable alternatives is easier in a pinch.

Leverage data

Teams need to keep close track of ingredients, processing parameters, and material performance so that they can use their R&D data to decide exactly what materials to make next. It is estimated that 80% of decision support is based on connecting structural properties to test data. However, transforming raw R&D data into the insights that propel innovation can be an onerous process.

That process is only getting more challenging as teams struggle to find informatics solutions that can handle their needs around data volume, sharing, automation, and analysis. Teams often need to also leverage their historical data and knowledge, which can be next-to-impossible when data are in many different states with digitization projects at different stages; key data may be difficult to find and use if they are scattered across various reports, data silos, and even in personal machines or paper notebooks.

Accommodate variance

C&M processes are often quite ingrained and can vary widely, with different testing regimens and equipment. For example, teams making shampoo are going to have a very different set of processing conditions and tests than teams making tires. Finding an informatics solution that accommodates variance—even within different groups in the same company—can be a struggle and researchers have often had to rely on cobbled together or less-than-ideal solutions.

Support sustainability

Over the past several years, the market has become increasingly committed to sustainability, as companies aim to create less waste, reduce pollution, and minimize environmental impact. For example, LEGO hopes to make most of its products from sustainable sugarcane by 2030, and Adidas has unveiled the world’s first performance shoe made from biodegradable biofibers. This focus on sustainability will have a wide-ranging impact—from ingredient selection, tracking and origin-tracing to later product testing.

Addressing challenges in chemicals and materials R&D

While C&M teams know that creating a future-ready lab is imperative, making that change can be a huge endeavor. Legacy solutions consisting of disconnected electronic laboratory notebooks (ELNs), laboratory information management systems (LIMS), and loosely-linked applications typically fall short.

Additionally, many companies are still early in their journey toward digitization and are still struggling with issues such as isolated systems and poorly integrated pieces. These companies invariably hope to improve upon issues such as data quality and transparency, process efficiency, and AI/ML preparedness.

A big part of the disconnect is that although some similarities exist between C&M and the life sciences, there are also some very impactful differences as well. Yes, the innovation cycle is similar on both sides (very broadly speaking); both life science and C&M researchers generally follow some sort of Make-Test-Decide workflow; they both want to be able to query data and see all inputs and outputs; they want to learn from what has been done before and track trends; they want to see what they made yesterday and what that can that tell them about what they need to make today. However, C&M research differs greatly in some key elements such as materials, how testing is done, shorter timelines, more varied equipment and so on.

Plus, within the C&M space there is an incredible amount of variance. Teams may be making batteries, semiconductors, advanced printing equipment, building insulation, milling products, etcetera. Although it may seem implausible that any single solution could successfully address such diverse needs, when looking closer, these specialty areas all share some common goals around areas such as experiment planning, project management, compositional data/mixtures, process exploration, and analysis and characterization.

The impact of an informatics system on a team’s success can be massive. By some estimates, 80% of decision support is based on connecting structural properties to test data. An informatics system needs to help teams accomplish this, both effectively and efficiently.

Unfortunately, as most C&M teams can attest to, this is not easy to achieve with disconnected ELNs, LIMS, and loosely integrated applications. Transforming instrument data into the insights that propel innovation is an onerous process, and that process is only getting more challenging as data-management issues grow.

As C&M teams face growing pressure to work faster, smarter, and more sustainably, it is now more important than ever for them to create a future-ready C&M lab. It is time to think beyond the traditional options of disconnected ELNs, LIMS, and applications, and to look toward next-generation technology that blends elements of each to deliver a powerful and flexible chemicals and materials R&D solution that helps accelerate data-driven C&M innovation.

About the Author

Melanie Nelson is the Director Of Product Management, Solutions and Integrations at Dotmatics, a leader in R&D scientific software connecting science, data and decision-making.