23 citations · 45 across the 6 of their papers we have counts for
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cond-mat.mtrl-sci2026★ 2 cited
High-quality, high-information datasets for universal atomistic machine learning
Cesare Malosso, Filippo Bigi, Paolo Pegolo +7
The quality, consistency, and information content of training data is often what determines the practical value of machine-learning models for atomistic simulations. Yet, many wide…
physics.chem-ph2026
Simultaneous Learning of Static and Dynamic Charges
Philipp Stärk, Henrik Stooß, Marcel F. Langer +4
Long-range interactions and electric response are essential for accurate modeling of condensed-phase systems, but capturing them efficiently remains a challenge for atomistic machi…