Transforming matter
The Smart Material team recently attended the prestigious NanoSeries 2024 Conference in Lisbon, Portugal, from June 17 to 19. This annual event brought together leading experts from the fields of nanotechnology and nanoscience, offering an exceptional platform to discuss the latest advancements and foster collaborations. The team’s involvement in this cutting-edge conference further solidifies Smart […]
Smart Material is excited to announce its participation in Kimiko, a new R&D project launched in collaboration with Navantia to tackle one of the biggest challenges in the naval industry: marine corrosion. This project aims to model vessel corrosion and develop new, more resistant steel alloys using the latest artificial intelligence (AI) and materials science […]
Smart Material is thrilled to announce its participation in NanomatIA, a groundbreaking new R&D project focused on accelerating the design and discovery of advanced materials for lithium-ion batteries (LIBs). This initiative, launched in collaboration with leading materials science companies and research centers, combines the power of artificial intelligence (AI), nanotechnology, and biotechnology to create high-performance, […]
Smart Material was proud to be part of the prestigious IUVSTA TEMTEM 2023 Workshop, held in Valencia (Spain) from December 18th to 21st. This event brought together global leaders in the characterization and development of advanced energy materials, focusing on solar energy, thermoelectric technologies, and battery storage solutions. Our platform, Smart Material, was at the […]
Introduction to AI Explainability In the realm of modern technology, Artificial Intelligence (AI) stands out as a paradigm-shifting field that affects various industries, from healthcare and finance to transportation and materials science. Nevertheless, as we further entrust AI with more intricate tasks, the pressing question of AI explainability and its importance arises. AI Explainability, sometimes […]
Introduction to best practices Machine learning (ML) models are becoming increasingly popular for a wide range of applications in materials science. Within this field, ML models are often classified as predictive and generative. Predictive models can predict certain properties or characteristics of unseen materials from a sufficient amount of material-property training data. Generative models go […]
