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physics.comp-ph2026
Frustrated supermolecules: the high-pressure phases of crystalline methane
Marcin Kirsz, Miguel Martinez-Canales, Ayobami D. Daramola +3
Methane is the simplest hydrocarbon, yet it exhibits an extraordinarily complicated series of crystal phases. Notably, the non-plastic phases have large unit cells with nearly, but…
physics.comp-ph2025
Tadah! A Swiss Army Knife for Developing and Deployment of Machine Learning Interatomic Potentials
M. Kirsz, A. Daramola, A. Hermann +2
The Tadah! code provides a versatile platform for developing and optimizing Machine Learning Interatomic Potentials (MLIPs). By integrating composite descriptors, it allows for a n…
physics.comp-ph2024
Understanding solid nitrogen through machine learning simulation
Marcin Kirsz, Ciprian G. Pruteanu, Peter I. C. Cooke +1
We construct a fast, transferable, general purpose, machine-learning interatomic potential suitable for large-scale simulations of . The potential is trained only on high qual…