To have a transformative impact, labs must reinvent workflows through digital technologies and skills, adopting a strong data culture. Innovation through digital-centric systems confidently produces new materials that meet customer specifications orders of magnitude faster than before, enabling broader business transformation.
Digital technologies are having a significant impact on R&D labs across all technology driven industries, in particular in chemistry and materials science. The bigger challenge is how to evolve R&D labs in a way that delivers value early and continuously, while creating an environment for innovation that can deliver orders of magnitude improvements in performance, and ultimately, business value.
The white paper ‘The Journey to Digital-centric Chemicals and Materials Laboratories’ posits that the transformation of R&D labs takes place in a well planned journey through five distinct levels, taking a holistic approach to data capture and usage, infrastructure and digital processes, introducing increasing levels of autonomy.
The levels are:
- Level 1: The Human-centric Lab
- Level 2: The Data-informed Lab
- Level 3: The Data-driven Lab
- Level 4: The Transforming Lab
- Level 5: The Digital-centric Autonomous Lab
The Transformed R&D Lab
Transforming a lab in today’s digital world is a journey. Scientists must acquire new skills, adopt a strong data culture and be empowered to bring digital innovation into the lab. Digital technologies that can rapidly evolve in lock-step with the lab must be adopted. An R&D system that is too rigid, inefficient, or adopted as a quick fix must be avoided, as it will be incapable of broader transformation and unable to adapt as business needs change.
When the lab arrives at a point where scientists can dial-in desired material or chemical properties, and samples with those properties are produced quickly and automatically, there has been a true transformation. It is now possible to develop highly customized products for each customer, bring speciality services into new markets, and stave off commoditization.
From there, the business must decide how to leverage this new capability. The challenge flips from a technical one of creating samples, to a business one of scaling production capacity, creating new customer-focussed digital sales tools, expanding into new markets and generating increased revenue – a good set of challenges to have.
Key to advancing to a Digital-centric Autonomous Lab is that technological and cultural changes progress concurrently. Technological initiatives generate value, while cultural and organizational initiatives accelerate value, increasing the potential beyond incremental steps, and ensuring a foundation for future progress. Once a given level has been mastered, the lab is positioned to move to the next.
At the final level, entirely new possibilities can be explored and a new future envisioned in line with broader digital business transformation goals.
Access the white paper here.
About the Authors
Chris Farrow, VP Materials Science Solutions, holds a Ph.D. in physics from Michigan State University and degrees in physics and mathematics from the University of Nebraska.
Michael Heiber, Manager, Materials Informatics, holds a Ph.D. in polymer science from The University of Akron and a B.S. in materials science and engineering from the University of Illinois at Urbana-Champaign with expertise in polymers for optoelectronic applications.
Top 5 Takeaways from the American Chemical Society (ACS) 2023 Fall Meeting: R&D Data, Generative AI and More
By Mike Heiber, Ph.D., Materials Informatics Manager Enthought, Materials Science Solutions The American Chemical Society (ACS) is a premier scientific organization with members all over…
There’s a long history of scientists who built new tools to enable their discoveries. Tycho Brahe built a quadrant that allowed him to observe the…
With the increasing importance of AI and machine learning in science and engineering, it is critical that the leadership of R&D and IT groups at...
From Data to Discovery: Exploring the Potential of Generative Models in Materials Informatics Solutions
Generative models can be used in many more areas than just language generation, with one particularly promising area: molecule generation for chemical product development.
Scientists gain superpowers when they learn to program. Programming makes answering whole classes of questions easy and new classes of questions become possible to answer….
OpenAI's ChatGPT, Google's Bard, and other similar Large Language Models (LLMs) have made dramatic strides in their ability to interact with people using natural language....
You know it. We know it. NumPy is cool. Pandas is cool. We can bend them to our will, but sometimes they’re not the right tools…
For many traditional innovation-driven organizations, scientific data is generated to answer specific immediate research questions and then archived to protect IP, with little attention paid...
Like most people, I mostly interact with Python using the default REPL or with IPython. Yet, I often reach for one of the Python tools…
Recently, I’ve been working on a new course offering in Enthought Academy titled Software Engineering for Scientists and Engineers course. I’ve focused on distilling the…