The Next Generation R&D in the Chemical and Material Science Industry

The future is already here, marked by advancements such as generative AI, quantum computing, cobots, avatar robots, and data mesh, yet these technologies remain unevenly distributed. Even the most innovative chemical companies must address the basics. Over 55% of the data generated in research labs is unstructured or described as dark data – not being used to derive any insights. Over 50% of researchers struggle to replicate their own experiments, and over 70% fail to replicate experiments of others. This hampers the innovative potential of chemical and material science companies. Additionally, addressing the demands of circularity, energy transition, and sustainability necessitates a shift in mindset, talent acquisition, and ecosystem development. To drive this future, innovation leaders in the chemical industry must ensure that their R&D is data- & tech-enabled, skill-based & talent-driven, and alliance- & ecosystem-powered. This approach will empower the next generation of R&D to deliver solutions for the future while providing an engaging environment for innovators.

The chemical industry today faces unprecedented challenges, from geopolitical uncertainty and rapid energy transition to what many are calling “a regulatory tsunami”. Yet no other industry is better positioned for the necessary transformations, such as circularity, mobility, personalized health and energy transition. All requiring new and advanced materials and systems developed by chemical and materials science companies in an ever-shorter innovation cycle.

This dynamic world leaves no room for the old ways and mistakes of chemical company R&D departments. There is no room for “black data” or low reproducibility, and there is no room for the physical experiments and scale-ups done the “old way” either. The best experiment is the one that is not done at all – neither on a lab bench nor in a scale-up facility. Instead, the best experiment is an in silico experiment, or a simulation suggested by an algorithm to narrow the experimental space as much as possible. The lab bench is reserved for a lucky few – a smaller number of selected, smart experiments that yield the most useful, reproducible data for rapid, data-based decision making, likely done by machines, and only monitored or reviewed by researchers. 

Your next best idea will most likely not be found in a lab, but rather in a complex ecosystem that follows the life cycle of a molecule or material from the cradle to the grave after several recycling lives. Your next best employee might not be a chemistry Ph.D. graduate, but a skilled machine learning engineer hired from a deep tech startup. And your next competitor may not be a chemical company, but a smart tech one.

Future R&D will need to address all these challenges – quickly. The chemical industry lags behind in the digitalization and the adoption of novel technologies. Chemical company R&D must change all four wheels while going full speed ahead to position itself for the future. This is a difficult transformation. 

Three elements must be addressed simultaneously to ensure success: 

Data- & tech-enabled R&D

A new and elevated R&D must emerge, one where a seamless merger of science and tech, physical and virtual worlds boost innovation and accelerate time to market. 

It is time to innovate smarter, not harder, using the advantages of emerging technologies such as (generative) AI, the Scientific IoT, robotics, and soon quantum computing and the metaverse. R&D departments will become more efficient, productive, and innovative. Simultaneously, R&D leaders must address the most common obstacles, such as heterogeneity of R&D data, risk aversion, isolation of R&D, legacy workflows and practices, and the lack of data visualization.

There are three priorities for R&D leaders to fully benefit from data- and tech-driven R&D:

• Boost modeling and simulation capabilities
• Embed deep tech into R&D workflows
• Bring R&D cybersecurity up to date

With ever-shorter innovation cycles, modeling and simulation capabilities are the true competitive advantage of a chemical company. Boosting these capabilities is a must for chemical and material science leaders to solve complex future challenges.

Embedding deep tech into R&D workflows at every stage of discovery will increase the innovative power of next-generation R&D. It’s all about merging the real and the virtual worlds for smarter, faster, and more scalable discovery.

R&D leaders must also bring R&D cybersecurity up to date. This is not only about preventing cyber attacks on the most critical infrastructure, but also about enabling a safe and secure collaboration within larger and more complex ecosystems.

Skill-based & talent-driven R&D

For true chemistry to happen, data- and tech-enabled R&D is only the first component. The next is having the right combination of skills and talents to drive discovery and commercialization. Faced with the complex challenges of chemical companies, the increasing complexity of emerging technologies, and the redefinition of work and the workforce, R&D departments must look to the larger workforce ecosystem for the necessary skills and talents. There are three elements to be addressed:

• Focus on skills and diversity in R&D teams
• Step into a new reality where physical and virtual worlds merge
• Embrace new ways of working

As the nature of work evolves and traditional jobs become increasingly fluid, focusing on skills allows R&D labs to adapt and thrive. By shifting away from strict roles, skill-based approaches enable employees to develop expertise and contribute to a range of responsibilities, fostering personal growth and job satisfaction. Innovative approaches and digital tools optimize the learning experience, enabling employees to upskill and reskill efficiently and effectively. By demonstrating a commitment to skill-based hiring and highlighting diversity and inclusion initiatives, R&D labs can differentiate themselves as desirable workplaces, attracting and retaining a diverse workforce that enhances overall performance.

While access to new technologies lays the foundation, it is the implementation and use by lab personnel that truly makes a difference. Establishing a symbiotic relationship between technology and people allows for these tools to be seamlessly integrated into daily lab practices, ultimately supporting the scientist's work. It is essential for labs to quickly adopt these technologies, harnessing momentum and ensuring that employees are well prepared to use them effectively. The journey towards people-centricity should include employees initiating change, communication, and training measures from the very start. This emphasis on people will drive innovation and enable scientists to push the boundaries of scientific research.

Embracing new ways of working in R&D labs is essential to increasing innovation and enabling faster time to market. Flexible working and collaboration models that engage talent from a broader workforce ecosystem, supported by enabling technologies such as cloud-based infrastructure and deep tech, are increasingly necessary. The impact of new ways of working extends beyond R&D departments and enterprise boundaries and is essential to solving complex challenges such as circularity, energy transition, and the journey toward a net zero economy.

Alliance- & ecosystem-powered R&D

The third ingredient requires stepping outside the box and joining or orchestrating larger ecosystems to drive purposeful innovation, growing the top line but also addressing significant economic and societal challenges. Collaboration and co-creation are essential for innovation in a complex environment. 

While chemical companies have in the past been good at balancing make, buy and ally to increase innovation, the bar for collaboration has been set even higher. It is not enough to manage an alliance portfolio, have an open innovation platform, or host an occasional innovation hackathon. R&D departments must prepare to collaborate and co-create in complex ecosystems.

The next generation of R&D is powered by alliances and ecosystems more than ever before. To succeed in this new reality, next generation R&D leaders must:

• Start thinking in ecosystems
• Get R&D data ready for data ecosystems.
• Explore R&D’s role in the monetization of ecosystems

The challenge faced, the brand value, and the mindset are aspects of ecosystems. The most dramatic cases might require a change in overall company culture to benefit from ecosystem thinking. 

FAIR data is a good starting point for getting R&D data ready for ecosystems. But setting up and growing an R&D data system requires much more -- there is no single recipe for success.

R&D plays a vital role in the monetization of ecosystems. It is a combination of chemistry and technology, atoms and bytes, the physical and virtual, to solve the complex challenges posed by sustainability and circularity goals.