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modelling of materials and machine learning. Experience in atomistic modelling (molecular dynamics, density functional theory) and machine learning is important, as well as a strong interest in pursuing
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results, further research is guided by trial and error with the goal of deriving intuitive trends. Data-driven approaches are attractive alternatives. Descriptors are used to characterize the molecular
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to characterize the molecular properties of catalysts together with statistical methods to derive predictive models for selective catalysis. In a data-driven approach, an initial set of reactions is analyzed and
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