Materials informatics (MI) involves using data-centric approaches, including AI and machine learning, to assist scientists and engineers in materials R&D. There are multiple strategic approaches and many notable success stories; adoption is accelerating, and this process has the potential to transform materials development, leading to huge cost savings and quicker routes to market for its users.
The new IDTechEx research report "Materials Informatics 2023-2033" provides key insights and commercial outlooks for this emerging field. Built upon technical primary interviews with 24 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 2033, with 13.7% CAGR expected until then. Case studies of numerous applications are outlined, highlighting the wide range of materials science areas where MI adds value. Analysis of the underlying technologies demystifies this fast-growing area of the R&D digital transformation.
Key areas of coverage in the IDTechEx report, "Materials Informatics 2023-2033". Source: IDTechEx
What Is Materials Informatics?
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. This can take numerous forms and influence all parts of R&D (hypothesis - data handling & acquisition - data analysis - knowledge extraction).
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). If integrated correctly, MI will become a set of enabling technologies accelerating scientists' R&D processes while making use of their domain expertise.
What Is New in Materials Informatics?
Awareness of the requirement for digital transformation in R&D is leading to an acceleration in the adoption of MI processes by materials industry players, from startups to established giants. Aside from growth in awareness, improvements in AI-driven solutions leveraged from other sectors and data infrastructures are driving growth.
MI adoption usually takes 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 and choosing the right path is essential.
External MI players are, in some cases, investigating strategic shifts from providing MI software/services to developing their own materials IP portfolios to capture more of the value chain. Kebotix is currently undergoing this shift: this was just one of many companies interviewed when producing this latest version of IDTechEx's report. This contrasts with the existing "default" strategy of offering MI as a SaaS product which is the end goal of many of its independent proponents. A difficulty here that needs solving is reassuring MI SaaS product users that their data is safe, and players, including Citrine Informatics, are investing significant resources into this.
2022 saw major funding raises for companies involved with digital materials R&D, including Wildcat Discovery Technologies' US$90 million Series D round. As the report outlines, this marks a sharp return to an increase in industry funding that has taken place after a short period of COVID-related downturn in 2020.
Key Questions Answered
The new IDTechEx report, "Materials Informatics 2023-2033", is now in its third update and is informed by first-hand interviews with the industry's major players. It answers questions including:
- What are the strategic approaches to MI and how do they compare?
- How do MI's practitioners solve the problem of sparse experimental datasets?
- Where and how is MI applied across a diverse range of fields of materials science?
- What companies are involved with MI and how do they stack up against one another?
- Which algorithmic approach is appropriate to solve various problems in MI?
- What have been the major developments in the field of MI in the last year?
- What should be expected for the future of MI adoption?
- How will MI and self-driving labs synergize to shape the future of materials R&D?
Market forecasts, player profiles, investments, roadmaps, and comprehensive company lists are all provided, making this essential reading for anyone wanting to get ahead in this field. To find out more about IDTechEx's technical and commercial analysis of the materials informatics industry, please visit www.IDTechEx.com/MaterialInformatics