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What can materials modelling do for you?
In 1929, Paul Dirac wrote that “the underlying physical laws for the mathematical theory of a large part of physics and the whole of chemistry are completely known”, referring to the fact that quantum mechanics, which had just been developed, provides the framework to understand any system made of atoms. He went on to write “the difficulty is that the exact application of these laws leads to equations much too complicated to be soluble [and] it becomes desirable that approximate practical methods of applying quantum mechanics should be developed”. Dirac was writing before the invention of the transistor, but now, thanks to modern computers, we can make much progress in fulfilling his vision. Density functional theory and related methods have become extremely successful at describing simple systems at moderate computational cost. Interest in now shifting towards more challenging systems, for example materials with structural complexity and materials containing heavy ions with active d and f electrons. Together with advances in machine learning, we are reaching a point where atomistic materials modelling is becoming increasingly predictive for many systems of experimental interest. In this lecture, I will ask the question: what can materials modelling do for you? Using examples from our recent work, I will describe how materials modelling can help rationalize increasingly complex experimental results and how materials modelling can make new predictions to guide experimental developments. Examples will span a broad range of topics, including superconductors, semiconductors, and topological materials, stressing the importance of an ongoing dialogue between experiment and computation.
Bio: Bartomeu received his undergraduate MSci Physics Degree from Imperial College London in 2011, and his PhD Degree in Physics from the University of Cambridge in 2015. He held a Henslow Research Fellowship at Robinson College between 2014-19, and spent 2015-2017 at Rutgers University on a postdoctoral fellowship. In 2018, he established an independent research group at the University of Cambridge as a Winton Advanced Research Fellow, and in 2020 he was appointed as the Gianna Angelopoulos Lecturer in Computational Materials Science in the Department of Materials Science and Metallurgy at the University of Cambridge.
