Designing a Materials Informatics Strategy

The first goal of your materials informatics initiative has to be to collect accurate, valuable data. Whether you already have years of manually recorded lab results or are starting from scratch, the goal is the same—create a methodology around recording, transforming, sharing, and learning from your organization’s materials data that relies on technology already validated by the most forward-thinking industries. In all fields where data is critical to success, you have to solve the “Garbage in, Garbage Out” problem first.

Where to Start: Getting Your Data in Order

Messy historical data shouldn’t prevent you from creating a better structure for the future. Begin compiling old data sources and taking note of everything you’ll want to eventually move into a single software solution that everyone can access. Some vendors, like Uncountable, can offer guidance on which sets of historical data are worth restructuring in your new system and may even provide services to help with the data cleaning and uploading process.

Even if you’re not ready to invest in materials informatics technology yet, it’s best to begin cataloging and preparing old data for transfer as soon as possible. Your ability to recover reproducible R&D data from an unstructured format decays rapidly over time, and capturing what you can will make the transition to a new system later much more seamless.

If your company lacks a reliable source of past experiment data, you’re not alone. Rather than fixate on having perfect historical information, collect what you can and focus your attention on what’s ahead. You’ll drive the most value by being proactive, adopting a materials informatics platform that gathers every new piece of data in a standardized, comprehensive way. 

Researching Your Technology Options

Before you dive into adjusting, re-entering, or consolidating historical data, consider the type of materials informatics solution you’ll be using it in. There are several tools on the market that help digitize and organize data, but it’s up to you to decide which will help you meet your goals.

What Are Electronic Lab Notebooks (ELNs)?

ELNs are the digital replacements for paper lab notebooks and basic spreadsheets. They provide a place to capture experiment data in every form, from details on raw materials to contextual drawings and tables.

Read more about electronic notebooks in Uncountable’s ELN evolution guide.

As R&D teams grow, they tend to run more experiments across many time zones, languages, and lines of business. An ELN can support their efforts to scale by providing a standard tool for streamlining and sharing lab notes. Not all will meet your particular organization’s goals, however, so it’s also good to know what they can’t do.

ELNs don’t typically support structured data, which means your team’s digital notes still won’t be useful for advanced analysis or feeding through machine learning models. They also don’t track information unrelated to specific experiments, including data on lab equipment, workflows and processes, and other important details related to R&D work.

If electronic lab notebooks are modernized to support structured data types, they can be paired with AI and machine learning technology to better approach the entire scope of your materials management strategy. Before we expand on this idea, let’s look at another key component to modernizing R&D—laboratory information management.

What Is a Laboratory Information Management System (LIMS)?

A laboratory information management system (LIMS) is software that helps you efficiently collect, organize, and share data from your R&D labs. It provides visibility and accountability at all steps in the testing process, including environmental, process, inventory, and equipment data that isn’t already tracked for each experiment. Some LIMS systems also help to connect directly to equipment in the lab, helping to automate the collection of test results.

Traditionally, LIMS were only meant to manage tasks and data collected in a lab, but newer solutions may add analytics and collaborative capabilities that help initiate a materials informatics initiative. Unfortunately, most LIMS fall short in their ability to build momentum based on past data. While any R&D team will benefit from keeping a clean record of test results for auditing purposes, most need more than that to see big time savings, as scientists still have to connect results to trial information.

Companies looking to produce the most innovative, high-performance, or sustainable products can move faster if they have a solution that helps them document all relevant data quickly and accurately, learn from previous experiments, and put the latest machine learning and AI to use. Neither an ELN or LIMS alone can accomplish this.

What Is a Materials Informatics Platform?

Uncountable takes the best features of an ELN and LIMS and adds cutting edge AI and machine learning, resulting in a single platform that makes materials informatics seamless and scalable.

The Uncountable platform extends traditional ELN capabilities, enforcing structured data entry for each experiment while still allowing unstructured notes and sketches to be taken alongside them. It also allows for data commonly collected in a LIMS to be standardized and shared across all stakeholders, using machine learning to produce actionable insights and predictions that help R&D labs work more efficiently.

Finally, once key R&D data is centralized and structured in a materials informatics platform, it becomes much easier (and worth the effort) to consider other databases and sources of knowledge within your organization that could be integrated to the platform, giving your R&D teams even more information at their fingertips to drive fast and innovative results. Some additional sources of data to consider include ERPs, public chemical databases, SDS/TDS information, customer feedback, third-party analysis, or other external lab results.

Uncountable’s team of experienced data scientists and materials experts can help you design an informatics strategy that best fits the goals of your organization. Request a personalized demo of the platform to learn how.

Standardizing and Connecting Materials Data

Research & development teams need to work with materials information they can trust. This means data that’s recorded in a standardized global format, safeguarded against manual deviations, and is stored somewhere secure and accessible to those who need it.

Depending on the solution you choose, you’ll want to make a list of all possible data types to collect and identify any knowledge gaps or obstacles before implementing your new technology. Using Uncountable, your list might look something like this:

  • Ingredients
  • Formulations
  • Lab conditions
  • Measurements (including curves, images, and other non-numeric test results) and test settings
  • Ingredient attributes / Materials properties
  • Inventory / Lots
  • Regulatory / Safety data
  • Lab scheduling / capacity
  • Scale-up parameters
  • Raw materials costs

You can ask your new vendor’s onboarding team how much they help with data entry and organization. An experienced materials informatics partner will offer guidance on how to link existing databases for maximum impact and where to spend resources to restructure key data for future use. This may include building APIs between internal platforms and third-party tools or   working with IT to export past data from various company databases.

Data in a materials informatics context can be any information that might impact project goals, processes, or outcomes in the R&D environment. It should allow scientists to build on each others’ work, using observations and test results to design future experiments and avoid wasted efforts.

Let your internal experts guide you. Which information is worth taking the time to structure first? What will enable scientists to do their job more efficiently, or allow them to explore brand new ideas productively? Where is time wasted or where are valuable hypotheses not tested, simply because the relevant data is currently difficult to find?

Planning for Change Across the Organization

Communicating your materials informatics strategy is as important as buying the technology to support it. To usher in a smooth transition, help R&D stakeholders understand why new processes are in place and how they will help further departmental goals.

It isn’t just the research team that should be included in this process. Materials informatics has the potential to connect data from other business functions that play a large role in R&D priorities. This might include information on customer demand for certain products, the capacity for testing in various onsite or third-party labs, processing and manufacturing parameters that may be optimized when a formulation moves into production, and the availability of raw materials from your suppliers. It can also be used to ensure the company is meeting regulatory requirements and safety standards.

To implement materials informatics at scale, you need just-in-time information from many different departments. The R&D team can use this data to avoid common bottlenecks and oversights that happen when there is no centralized system in place.

Building a Culture of Continuous Innovation in Materials Design

Once you have the foundations of your materials informatics strategy, there’s plenty more to keep you informed and innovating. 

Benchmark Against Our Maturity Index

Use this timeline to assess where you should be after starting the implementation process:


  • Map existing R&D data and relevant databases
  • Implement materials informatics platform & train pilot group
  • Perfect key workflows
  • Train broader R&D team
  • Begin connecting other key live databases to materials informatics platform
  • Finalize integrations with other key live databases
  • Advanced training for R&D Teams on experiment design with new statistical tools
  • Onboard all teams across all global regions of and harmonize their data to allow for standardization and rationalization
  • Any new systems or equipment that are implemented should have the goal of increasing ease and reliability of gathering data
Realized Benefits
  • R&D Teams see time savings of hours/week with easier searchability, data analysis & visualizations
  • Avoid unnecessary repeat experiments with better searchability & collaboration tools across teams/locations
  • AI & Advanced Statistical Analysis can be applied to high priority projects to decrease experimentation time by 3x or more
  • Apply AI & Advanced Statistical Analysis to most development projects
  • Scaled time and resource savings across the organization from better data management and collaboration
  • AI used to guide decisions across all phases of product lifecycle, including research, development, production troubleshooting
  • True global collaboration enabled through standardized software systems and nomenclature

Additional Materials Informatics Resources

Stay up-to-date with these helpful resources from Uncountable and our partners:

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