Materials Informatics: Solving Challenges in Materials R&D

Materials informatics is transforming how materials companies approach R&D, bringing data-centric approaches, including AI and machine learning, to materials science to reduce costs and develop new products faster. IDTechEx's report on the topic, "Materials Informatics 2024-2034: Markets, Strategies, Players", delves into each possibility, offering insightful information about key players and the future of the technology.

 

 

Sourcing data for materials informatics can be challenging and costly. Physical experiments naturally provide the most accurate data. Materials informatics can reduce the number of experiments needed to get results, but sourcing enough data this way to train machine learning models can get expensive fast.

 

Simulation data is an important alternative to physical experiments, though it is not a silver bullet. Although normally cheaper than physical experimentation, simulation is still expensive, especially as accuracy increases, and results may not always match up to reality. A hybrid approach of physical experiments and simulation ensures accuracy by validating results while increasing the volume of data available to train machine learning models.

 

 

Pre-existing data repositories, alongside mining data from scientific literature and patents, can provide low-cost options to gain vast amounts of data. However, data mining has fundamental challenges with accuracy and may require a lot of human involvement, and there's no guarantee that the data needed for a project even exists in a data repository. Many companies have told IDTechEx that they cannot trust data they did not generate themselves since there are otherwise so many unknowns not captured, which can affect results significantly.

 

This is a key reason why materials and chemicals companies have realized their back catalogs of experimental data are almost as valuable an asset as their ranges of products. By applying materials informatics principles, pre-existing data can be given new value, just so long as it can be pulled into one place.

 

 

Even when you have the data needed to run a materials informatics project, it's likely to be sparse, biased, and notoriously noisy. Compared to a more conventional "big data" AI project, such as predicting people's online shopping habits, creative approaches often need to be applied to get results. Different companies will have their own approaches here.

 

For example, Bay Area firm NobleAI uses multiple machine learning models working together in an ensemble with other modeling strategies, reflecting the different length scales and physical regimes that materials modeling problems take place over. Another approach from Cambridge, UK-based Intellegens uses a special class of neural network, which is adept at predicting missing values in its own inputs, helping to solve issues with data availability. Diversity in methods helps keep the materials informatics market dynamic and rapidly evolving, although the continuous need to innovate on new software features can act as an expensive barrier to profitability for players in this field.

 

 

Materials informaticians were doing generative AI before it was cool, but some of the most-hyped technologies to come out of the current AI boom are having a great impact here. Large language models (LLMs), like the GPT4.0 model behind ChatGPT, are showing great promise in making materials informatics easier to work with. GPT4.0 could allow users to intuitively add relationships between material properties to a model using text, something which Citrine Informatics has demonstrated, making it simpler for materials scientists to apply their domain expertise to materials informatics projects. This could greatly benefit companies by reducing the cost of onboarding new clients and lowering one of the hurdles to profitability.

 

 

In-house materials informatics could be on the rise in the future, potentially decreasing the total addressable market for MI services, which is why training services and investments in existing employees could prove to be profitable. Enthought is helping companies use open-source tools to develop their own MI strategy, which could be a smart and sustainable option going forward.

 

Materials informatics will continue to be used for attaining more accurate results with fewer experiments and enabling the inverse design of new materials by meeting a set of desired properties, competing with the intelligence of scientists in the R&D sector. Its ability to respond to difficulties in supply chains by selecting alternatives to certain materials when they become unavailable makes this technology unlike any other.

 

 

For more information, please see IDTechEx's report on the topic, "Materials Informatics 2024-2034: Markets, Strategies, Players". Downloadable sample pages are available for this report.

 

For the full portfolio of advanced materials market research available from IDTechEx, please see www.IDTechEx.com/Research/AM.

IDTechEx provides trusted independent research on emerging technologies and their markets. Since 1999, we have been helping our clients to understand new technologies, their supply chains, market requirements, opportunities and forecasts. For more information, contact research@IDTechEx.com or visit www.IDTechEx.com.