Materials Informatics

The battery industry is facing increasing pressure to develop new chemistries and materials to keep up with rising energy density and sustainability demands. The traditional materials discovery process is both expensive and time-consuming. Machine learning methods, specifically materials informatics, could provide a necessary avenue for accelerating the battery development process.

As the electric vehicle (EV) and battery energy storage system (BESS) industries grow, requirements for the batteries that power them become more demanding. To achieve more energy-dense batteries that aren't unsustainably expensive, faster battery development is needed, as well as more efficient management, manufacturing, and recycling methods. Artificial intelligence (AI) will play an important role in achieving these goals.

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.

The risk of missing out on the competitive advantage quantum computing offers is rising. Governments and private investors worldwide are placing multi-billion-dollar bets that the industry will produce huge long-term returns. Yet, for this to be realized, the theoretical advantage of quantum computers must be translated into real-world commercial value. In this article, IDTechEx explores which applications are being developed today across the materials, chemical, automotive, finance, and healthcare industries.

The impact of machine learning and data science techniques on the materials industry has grown exponentially since IDTechEx started covering the field of materials informatics in 2020, impacting the real world from lightweight alloys to new battery chemistries.

Materials informatics applies data-driven strategies to materials R&D. Long before generative AI technology reached peak hype, it had a long history of success in this field. A common approach is to use machine learning models trained on databases of material structures and properties, which then capture the underlying structure-property relationship.

Materials informatics represents the data revolution's impact on materials R&D. Data-centric approaches, including AI and machine learning, are transforming the way materials scientists and engineers work, getting materials to market faster and driving development in new directions. A range of strategic approaches have emerged, with leading players and commercialized materials developed via data-driven methods emerging.

Thursday 21 March 2024 - Materials Informatics: The Data Revolution and AI Boom come to Materials R&D - The latest commercial developments in the field, including the impact of new industry interest in the age of AI; Assessment of the different strategic approaches being adopted; The key technology pillars required for a MI approach; Industry pain points; Application case studies

Materials informatics is the application of data-driven methods, including machine learning techniques, to the field of materials science. This important part of the digital transformation toolbox for the materials industry has wide-ranging benefits, but the ability to enhance the sustainability of materials and their manufacturing processes could be the most impactful of these.