Materials Informatics represents an R&D paradigm shift by fundamentally accelerating the time from innovation to market. It represents the digital transformation of the materials industry, with AI driving material development. There are multiple strategic approaches and many notable success stories; missing this transition will be costly.
This report provides key insights into the market, forecasting its growth to 2034. Readers will get a detailed understanding of the players, business models, technology, and the application areas. Over 30 company profiles are included, giving primary insights into this fast-evolving industry.
Materials informatics (MI) involves applying data-centric approaches for materials science R&D, including machine learning. There are multiple strategic approaches and many notable success stories; adoption is accelerating and missing this transition will be costly.
This report provides key insights and commercial outlooks for this emerging field. Built upon technical primary interviews with 27 players, readers will get a detailed understanding of the players, business models, technology, and strategies in this industry. The revenue of firms offering MI services is forecast to 2034, with 11.5% CAGR expected until then. The impact of the ongoing AI boom is considered and numerous relevant projects across materials science are covered. Analysis of the underlying technologies demystifies this fast-growing area of digital transformation in R&D.
What is materials informatics?
Image: The major classes of project in materials informatics. Source: IDTechEx.
Materials informatics is the use of data-centric approaches for the advancement of materials science. This can take numerous forms and influence all parts of R&D (hypothesis – data handling & acquisition – data analysis – knowledge extraction).
Primarily, MI is based on using data infrastructures and leveraging machine learning solutions for the design of new materials, discovery of materials for a given application, and optimization of how they are processed.
MI can accelerate the “forward” direction of innovation (properties are realized for an input material) but the idealized solution is to enable the “inverse” direction (materials are designed given desired properties).
This is not straightforward and is emerging from its nascent stage. In many cases, the data infrastructure is not comprehensive and MI algorithms are often too immature for the given experimental data. The challenge is not the same as in other AI-led areas (such as autonomous cars or social media), the players are often dealing with sparse, high-dimensional, biased, and noisy data; leveraging domain knowledge is an essential part of most approaches.
Contrary to what some may believe, this is not something that will displace research scientists. If integrated correctly, MI will become a set of enabling technologies accelerating scientists’ R&D processes whilst making use of their domain expertise. For many, the dream end-goal is for humans to oversee an autonomous self-driving laboratory; although still at an early-stage there have been key improvements, spin-out companies formed, and success stories all facilitated by MI developments.
Why now?
This is not a new approach; many sectors have adopted similar design approaches for decades. But there are three main reasons why this transformative technology is impacting the materials science space right now:
- Improvements in AI-driven solutions leveraged from other sectors. This includes the impact of large language models in simplifying materials informatics.
- Improvements in data infrastructures, from open-access data repositories to cloud-based research platforms.
- Awareness, education, and a need to keep up with the underlying pace of innovation. The AI boom has only accelerated this need.
IDTechEx has identified three repeated advantages to employing advanced machine learning techniques into the R&D process: enhanced screening of candidates & scoping research areas, reducing the number of experiments to develop a new material (and therefore time to market), and finding new materials or relationships. The training data can be based on internal experimental, computational simulation and/or from external data repositories; enhanced laboratory informatics and high throughput experimentation or computation can be integral to many projects.
This report looks at the key progressions in machine learning for MI, the success stories, and how end-users are actively engaging with this.
What are the strategic approaches?
Ignoring this R&D transition is a major oversight for any company that designs materials or designs with materials: awareness of the potential significant missed opportunities in the mid- to long-term is growing rapidly. This could be when bringing competitive products to market, developing versatility in the supply chain, finding next-generation opportunities, or generating the ability to diversify a business unit or material portfolio.
Numerous players have already begun this adoption with three core approaches: operate fully in-house, work with an external company, or join forces as part of a consortium. Each of these approaches is appraised in detail in the report; choosing to start the adoption of MI is important, choosing the right path is essential.
The external MI players can come from numerous starting points, as outlined in the figure below. There is also the option for MI players to become a licensing company with a strong advanced material portfolio and also for end-users to offer MI as a service. Geographically, many of the end-users embracing this technology are Japanese companies, many of the emerging external companies are from USA, and the most notable consortia and academic labs are split across Japan and the USA.
Image: Categorizing materials informatics industry players. Source: IDTechEx
Interview based profiles of many key companies are included within this IDTechEx report.
What will I learn from the report?
This market report is released at a point in time where the 10-year outlook is prime for rapid adoption, with the market for external MI players expected to exceed US$700M by 2034. This report goes far beyond what is available on the internet, providing key commercial outlooks based on primary interviews coupled with expertise on both this topic and numerous of the relevant application areas.
In recent years there has been significant progression in external companies providing MI solutions, more key partnerships and end-user engagements, new consortia and academic advancements, and new companies emerging. All of this is tracked, explained and analyzed throughout this industry leading report on the topic.
Source: idtechex.com