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Accurate prediction of structural and mechanical properties on amorphous materials enabled through machine-learning potentials: a case study of silicon nitride2024 arXiv (Cornell University) preprint Materials Science Machine Learning in Materials Science Open access
Ganesh Kumar Nayak, Prashanth Srinivasan, Juraj Todt, Rostislav Daniel, Paolo Nicolini, David Holec
1citations -
Computing formation enthalpies through an explainable machine learning method: the case of lanthanide orthophosphates solid solutions2024 Frontiers in Applied Mathematics and Statistics article Materials Science Machine Learning in Materials Science Open access
Edoardo Di Napoli, Xinzhe Wu, Thomas Bornhake, Piotr M. Kowalski
1citations -
SALTED: a symmetry-adapted machine-learning program for predicting electron-densities in molecules and materials2026 arXiv (Cornell University) preprint Materials Science Machine Learning in Materials Science Open access
Zekun Lou, Alan M. Lewis, T. Bernhard, Lukas Seifert, Agustin Salcedo, Florian Kleemiss, +2 more
0citations -
Predicting Grain Boundary Segregation in Magnesium Alloys: An Atomistically Informed Machine Learning Approach2024 RWTH Publications (RWTH Aachen) article Materials Science Magnesium Alloys: Properties and Applications Open access
Zhuocheng Xie, Achraf Atila, Julien Guénolé, Sandra Korte‐Kerzel, Talal Al‐Samman, U. Kerzel
2citations