Beecroft Building, Department of Physics, University of Oxford, Parks Road, Oxford, OX1 3PU
Professor Nicola Marzari, University of Cambridge
Prof Marina Filip
Abstract
Materials underpin many of the technologies that drive our economy and sustain our societies. Providing reliable, predictive descriptions of their behaviour from first principles remains a defining challenge for condensed-matter physics and materials science — one made increasingly urgent by the convergence of high-performance computing, machine learning, and artificial intelligence, which are transforming the scale and scope of materials design and discovery.
I will discuss recent progress along three directions. First, I will address predictive accuracy, focusing on functional theories of the spectral density to address electronic excitations and correlations.
Second, I will consider realistic complexity, introducing mesoscopic transport theories that bridge microscopic equations and macroscopic diffusion. Third, I will discuss materials informatics, emphasizing automation, agency, and autonomy in campaigns of materials discovery.
These ideas will be illustrated with three case studies: the Mott–Hubbard and charge-transfer regime of transition-metal monoxides; hydrodynamic heat transport in bulk compounds and micro-devices; and in charting the electronic structure of experimentally known inorganics, searching for novel properties and improved performance.