The prediction of complex materials properties became possible recently by workflows that integrate high-throughput density functional theory calculations, training of machine learning potentials and subsequent atomistic simulations. In my talk I will give examples of our work from the computation of phase diagrams to the prediction of the structure of nano clusters. I will then discuss our workflows step by step and explain decisions and/or approximations that need to be made at each step. This will allow me to highlight remaining challenges and to give estimates of required computational resources.
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