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cond-mat.mtrl-sci2026
Comparative study of ensemble-based uncertainty quantification methods for neural network interatomic potentials
Yonatan Kurniawan, Mingjian Wen, Ellad B. Tadmor +1
Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near first-principles accuracy at substantially reduced computational cost, making them powerful t…
cond-mat.mtrl-sci2025
Scalable Etch-Free Transfer of Low-Dimensional Materials from Metal Films to Diverse Substrates
Kentaro Yumigeta, Muhammed Yusufoglu, Mamun Sarker +12
Low-dimensional materials hold great promises for exploring emergent physical phenomena, nanoelectronics, and quantum technologies. Their synthesis often depends on catalytic metal…