Nowadays, the great performance of Rare-Earth (RE) based permanent magnets (PMs) makes them essential in many technological applications (electric motors, generators, etc.) , leading to a strong dependency on critical raw materials as RE elements Nd, Sm and Dy. Finding viable alternatives based on cheap high-performance RE-free PMs has become an important issue but also a major scientific and technological challenge. To this end, the experimental exploration of new PMs begins to be assisted and guided by computational approaches thanks to their advances in calculation speed, accuracy and reliability [2-4]. In 2011, it was launched the Materials Genome Initiative (MGI) alongside the Advanced Manufacturing Partnership to help businesses discover, develop, and deploy new materials by promoting and supporting the third generation of material electronic structure databases. As a result, large open material databases (AFLOW, Materials Project, JARVIS, etc) with many functionalities have been created, providing a powerful tool for discovering and designing novel materials through machine learning and data mining techniques. Recently, based on the philosophy and standards of MGI, we have developed the Novamag database [5,6] which aims to complement and extend existing MGI databases for the specific case of PMs by making publicly available many theoretical and experimental results of magnetic materials studied in the H2020 European project Novamag. In this talk, we will show the main features and applications of this new database. For instance, we will discuss about the computational screening that we have recently performed for identifying novel magnetic Fe-Ta structures with high magnetocrystalline anisotropy energy  through the Novamag database tools. This approach reveals two interesting hard magnetic phases Fe5Ta2 (space group 156) and Fe6Ta (space group 194) with performance comparable to the state of the art RE-based PMs like SmCo5 and Nd2Fe14B.
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