The myriad of R&D challenges facing Chemicals and Materials (C&M) teams has long been amplified by a lack of off-the-shelf informatics offerings that truly address these teams’ unique needs. In fact, finding or building an informatics solution to support nimble, data-driven C&M workflows is often a struggle in its own right. Why? Here are three key reasons.
1. C&M is Not Life Science
Many off-the-shelf informatics solutions have been built for the life sciences. While there are some similarities between C&M and the life sciences, there are 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 that an informatics solution must accommodate, including:
Materials: What is made, and what is important to record, is obviously very different.
Testing: There is a great deal of difference in not just what is tested but also how it is tested.
Timelines: Time cycles are often much shorter and this makes agility key to successfully responding to internal and external market needs.
Equipment: Just as the processes are more varied, the equipment is more varied; teams often utilize a wide variety of equipment across the workflow.
Tracking: Thorough record keeping is needed to track what and how a product was actually made, versus how it was originally planned.
2. C&M Is Incredibly Diverse
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. While it may seem implausible that any single solution could successfully address such diverse needs, when looking closer, these speciality areas all share some common goals around:
Experiment Planning & Project Management: Most research teams organize their data into projects so that they have one place to unite everything—from research insights to customer interactions to planning documents. It is important that a solution accommodate this approach.
Compositional Data / Mixtures: Tracking what and how much of an ingredient is in a composition is imperative. Teams often want to record ingredient lists, manage inventory, samples and test results, and define ingredient properties. Beyond that, they also want to support knowledge extraction and knowledge-based design.
Process Exploration: Teams need to record what they did to make their product, including the ingredients, procedures, and conditions. Process capture is key, from recording data points (details of heat, pressure, extrusion, mixing, etcetera) to graphical representations to ingredients.
Analysis & Characterization: It is also important to consider what questions need to be asked of the data, how answers should be presented (e.g., a report, graphs, dashboard, etcetera), and what underpinnings are needed to get there (data standardization, specifical analytics functionality, report automation). Teams may want to ask, “What changes do we need to make to our inputs to get closer to the desired outputs?” or “What do I need to change in my formulation to make the material desired with the properties desired?” Before they can answer these questions, they will first need a clearly defined picture of how they will be able to attain those insights from their experiments and data.
3. C&M Legacy Solutions Fall Short
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. Teams today need to consider how the informatics solutions they have in place will address:
Data Volume - Labs today have more and more instruments generating huge amounts of data. That data must be securely stored and cleanly organized. Teams no longer just want end-results; they want all raw data. They want to be able to trace that data every step of the way. They want to be able to quickly find that data when they need it.
Data Sharing - Teams are frequently spread out across locations, often in different countries, and they are often working with unique workflows and tools. Data must be standardized, well-labeled, interoperable, and easy to securely share.
Data Automation - Teams are trying to automate as much of their processes as possible in order to get through their workflow faster. This is challenging when an informatics solution is comprised of loosely connected pieces.
Data Analysis and Modeling - Analysis is often a recursive process, with teams frequently going back to reprocess and reanalyze data; for example, they may need to reexamine existing data when looking to repurpose existing products for new uses. To make sense of their data, teams frequently take advantage of data modeling—from looking at trends in graphs to basic statistical analysis. Many teams are also looking to adopt advanced technologies like artificial intelligence (AI) and machine learning (ML) to accelerate innovation, improve performance, and drive advancements. The first step toward enabling sophisticated data analysis and modeling, including AI and ML, is getting all R&D data into a standardized, machine-readable format. Teams need an informatics solution that will help them do that.
Creating a Future-Ready Lab with the Dotmatics
Inspired by the experiences of our C&M customers, we have created the Dotmatics Chemicals and Materials Solution. This off-the-shelf solution offers a package of capabilities, including ELN and ingredient-, sample- and data-management functionality. The solution supports rapid, data-driven materials innovation by providing a flexible end-to-end workflow that covers the entire “Make-Test-Decide” innovation cycle. And, most importantly, it captures all the context of the C&M workflow, enabling researchers to connect ingredients, processing parameters, and material performance, so that their R&D data can tell them what materials to make next.
What Makes Dotmatics C&M Solution Unique?
Data-driven platform
Provides unified view on all data
Offers natively interoperable components
Implements roles and workflows
Integrates third-party data sources and applications
Configuration-driven interface
Delivers flexible domain models via templates
Simplifies adoption via workflow implementation
Eliminates data silos
“Data-out” capabilities
Provides ability to query against all aspects of a domain model
Integrates round trip: query, visualization, decision support, report
Learn More: Watch the On-Demand Webinar
Watch the webinar, “How to Accelerate Chemicals and Materials R&D with Next-Gen Technology,” and follow along as we:
Review the challenges facing Chemicals and Materials R&D teams
Explore why it’s difficult to find an informatics solution to address those challenges
Show how Dotmatics’ next-gen informatics solution has the power and flexibility needed to accelerate nimble, data-driven C&M innovation.
