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MiAD: Mirage Atom Diffusion for De Novo Crystal Generation
Andrey Okhotin, Maksim Nakhodnov, Nikita Kazeev +3
In recent years, diffusion-based models have demonstrated exceptional performance in searching for simultaneously stable, unique, and novel (S.U.N.) crystalline materials. However,…
Energy Underprediction from Symmetry in Machine-Learning Interatomic Potentials
Wei Nong, Ruiming Zhu, Zekun Ren +7
Machine learning interatomic potentials (MLIAPs) have emerged as powerful tools for accelerating materials simulations with near-density functional theory (DFT) accuracy. However,…
Inverse Design of Broadband Antennas for Terahertz Devices Based on 2D Materials
M. Lukianov, A. Maevskiy, N. Kazeev +5
Terahertz (THz) technology, a cornerstone of next-generation high-speed communication and sensing, has long been hindered by impedance mismatch challenges that limit device perform…