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compositionally complex circular steels. As a PhD researcher, you will: Perform Density Functional Theory (DFT) calculations to model hydrogen-tramp element co-segregation at grain boundaries and phase boundaries
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science. Electronic structure calculations (e.g. DFT or tight-binding methods), or thermodynamic modelling (e.g. statistical mechanics, MD or CALPHAD). Scientific data analysis. Interdisciplinary research
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for molecular magnetic materials lags behind these experimental breakthroughs. DFT fails to capture strong correlation, while wave function-based methods are computationally prohibitive for strongly-correlated
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highly challenging task. The project uses Machine Learning (ML), in combination with DFT and state-of-the-art Boltzmann transport methods, to predict, accelerate, and scale the computation of electronic
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made significant progress in this direction by merging machine learning interatomic potentials (MLIPs) trained on density functional theory (DFT) data, and enhanced sampling techniques to reach the