Publications by authors named "Lukas Kyvala"
Diffusion and Coalescence of Phosphorene Monovacancies Studied Using High-Dimensional Neural Network Potentials.
J Phys Chem C Nanomater Interfaces
December 2023
Article Synopsis
- This study examines how defects, specifically monovacancies, affect the properties of phosphorene, a two-dimensional material, using advanced molecular dynamics simulations.
- Researchers used high-dimensional neural network potentials to accelerate simulations while maintaining accuracy compared to traditional methods like density functional theory (DFT).
- Findings indicate that monovacancies are highly mobile, primarily moving in the zigzag direction, and can merge into more stable divacancies through various pathways, highlighting the complex nature of defect dynamics in two-dimensional materials.
Optimizing the architecture of Behler-Parrinello neural network potentials.
J Chem Phys
September 2023
The architecture of neural network potentials is typically optimized at the beginning of the training process and remains unchanged throughout. Here, we investigate the accuracy of Behler-Parrinello neural network potentials for varying training set sizes. Using the QM9 and 3BPA datasets, we show that adjusting the network architecture according to the training set size improves the accuracy significantly.
View Article and Find Full Text PDFA new type of uranium binary hydride, UH, with the CaF crystal structure, was synthesized in a thin-film form using reactive sputter deposition at low temperatures. The material has a grain size of 50-100 nm. The lattice parameter a = (535.
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