ausblenden:
Schlagwörter:
Forecasting; Machine learning; Magnetic materials; Magnetoresistance; Basic application; Hard magnets; High-temperature superconductivity; Inverse designs; Machine-learning; Material predictions; Material science; Research fields; Scientific achievements; Through put; Topology
Zusammenfassung:
Most of the time great scientific achievements are accidental, this is also true for material science, examples are high temperature superconductivity, colossal magnetoresistance and hard magnets. So far, inverse design has been realized only to a limited extent. Experience and intuition in materials science have also played a role in the discovery of new materials for basic science and application. The new research field of topology, that it is based on a single-particle model has been very successful in terms of the theoretical prediction of topological properties. However, the prediction of topological magnetic materials is already more challenging. In my talk I would like to illuminate the active research field of machine learning from my point of view for the areas of "new materials for magnetism and topology". © 2023 IEEE.
