most citedMachine Learning-based estimation and explainable artificial intelligence-supported interpretation of the critical temperature from magnetic ab initio Heusler alloys data

3 citations · 4 across the 2 of their papers we have counts for

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cond-mat.mtrl-sci2023★ 1 cited

Application of batch learning for boosting high-throughput ab initio success rates and reducing computational effort required using data-driven processes

Robin Hilgers, Daniel Wortmann, Stefan Blügel

The increased availability of computing time, in recent years, allows for systematic high-throughput studies of material classes with the purpose of both screening for materials wi…

cond-mat.mtrl-sci2023★ 3 cited

Machine Learning-based estimation and explainable artificial intelligence-supported interpretation of the critical temperature from magnetic ab initio Heusler alloys data

Robin Hilgers, Daniel Wortmann, Stefan Blügel

Machine Learning (ML) has impacted numerous areas of materials science, most prominently improving molecular simulations, where force fields were trained on previously relaxed stru…