Artificial Intelligence is reshaping the way new materials are discovered, tested and deployed. The latest insights from IDTechEx reveal how materials informatics is driving innovation across industries while setting the stage for a more data-driven, collaborative future.
Since IDTechEx began tracking the field of materials informatics in 2020, the influence of Artificial Intelligence (AI), Machine Learning and data science on the materials sector has grown exponentially. These technologies are driving innovations across applications from advanced alloys to next-generation battery materials. Yet despite rapid technological progress, the fundamental principles for successful materials informatics implementation remain largely unchanged.
The latest IDTechEx report, Materials Informatics 2025-2035: Markets, Strategies, Players, provides insights for organisations looking to deploy materials informatics strategies, whether as end-users or software providers. This article explores the key considerations shaping the evolving field.
The critical balance between data quality and quantity
The adage ‘garbage in, garbage out’ remains particularly relevant in materials informatics. While Machine Learning models require high-quality training data, the sheer volume of data also plays a decisive role in predictive capabilities. Organisations typically source data through several approaches, each with distinct trade-offs between accuracy and scale.
Experimental data remains the gold standard for reliability, though its high cost often restricts how much can be collected. Computational simulations offer a more scalable alternative, but their accuracy depends on the underlying models and may not fully capture real-world behaviours.
Public databases and literature-mined datasets provide vast quantities of data but often suffer from bias, incomplete records and a lack of negative results. Some large industrial players have opted to disregard external datasets entirely, though this is impractical for organisations with more constrained resources.
Innovative solutions are emerging. For example, Japan’s Preferred Computational Chemistry has developed the Matlantis platform, which uses a graph neural network trained on density functional theory simulations. By expanding the dataset to include unstable molecular configurations near known stable structures, the system achieves broader coverage of potential combinations while dramatically accelerating computation times, delivering results in seconds rather than hours or months.
Overcoming data fragmentation in materials R&D
Effective data management remains a major obstacle for materials companies pursuing Digital Transformation. Many still rely on fragmented systems, from Excel files to paper-based records, creating inefficiencies and hindering collaboration.
Electronic lab notebooks and laboratory information management systems provide structured ways to organise data, but inconsistent adoption across different units can lead to silos and redundant costs. To address this, providers such as Uncountable, MaterialsZone and Albert Invent have developed platforms that integrate data management with advanced analytics. These systems often feature API connectivity with common laboratory software, easing transitions from legacy workflows while preserving digital infrastructure.
Specialised AI approaches for materials discovery
Machine Learning applications in materials science extend far beyond conventional data analysis. A promising area is inverse design – identifying material compositions that exhibit target properties. This poses computational challenges, as datasets often contain high-dimensional information with missing values from multiple sources.
Companies such as Citrine Informatics employ active learning, where AI models propose candidate materials that are then experimentally validated, with results feeding back into the system to refine predictions. Other innovators, including Intellegens, have developed modified neural networks capable of handling incomplete datasets by iteratively estimating missing properties. These specialised approaches underscore why materials informatics requires tailored solutions rather than generic Machine Learning tools.
User-centric design as a competitive advantage
Analytical tools provide little value if inaccessible to the researchers who need them. Successful platforms must bridge the gap between data scientists and materials specialists, offering intuitive interfaces for experimentalists while retaining advanced functionality for AI experts.
Leading providers are prioritising flexible visualisation tools and adaptable workflows that support users of varying technical expertise. Lowering the barrier to adoption enables organisations to demonstrate benefits before committing to large-scale deployments.
Strategic insights for the coming decade
The fifth edition of IDTechEx’s Materials Informatics 2025-2035: Markets, Strategies, Players report delivers comprehensive analysis of this rapidly evolving sector, including market forecasts, profiles of key players, investment trends and technology roadmaps. Drawing on interviews with leading companies, the report provides essential guidance for organisations seeking to capitalise on materials informatics advancements.
