Descrição
Machine Learning for 2D Materials Discovery: stability, topology, and moiré twistronics One of the main goals and challenges of materials discovery is to find the best candidates for each interest property or application. Machine learning rises in this context to efficiently optimize this search, exploring the immense materials space, consisting of simultaneously the atomic, compositional, and structural spaces. This strategy is much more efficient than the Edisonian trial-and-error approach while a few orders of magnitude faster. In this talk, I will present the use of machine learning for the discovery and design of two-dimensional (2D) materials, at three different levels: i) feasibility (stability); ii) properties (topology); and iii) design (twistronics).Quando?
