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Improved atomistic Monte Carlo models based on ab-initio -trained neural networks: Application to FeCu and FeCr alloys
(American Physical Society, 2017-06)
We significantly improve the physical models underlying atomistic Monte Carlo (MC) simulations, through the use of ab initio fitted high-dimensional neural network potentials (NNPs). In this way, we can incorporate energetics ...
Angularly resolved two-photon above-threshold ionization of helium
(American Physical Society, 2019-02)
Angularly resolved two-photon single ionization yields of helium resulting after the interaction with an ultrashort XUV pulse are obtained by numerically solving the full dimension time-dependent Schrödinger equation. The ...
FP-LAPW calculations of the EFG at Cd Impurities in rutile SnO2
(Kluwer Academic/Plenum Publ, 2001-11)
We report an ab initio study of the electric-field gradient (EFG) at Cd impurities located at the cation site in the semiconductor SnO2 (rutile phase). The study was performed with the WIEN97 implementation of the FP-LAPW ...
ChemInform Abstract: First Principles and Experimental Studies of the Structural and Magnetic Ground State of the Ternary Compound MnFe 3 N
(John Wiley & Sons, 2016-08)
Ab initio DFT calculations, XRD, Moessbauer spectroscopy, and magnetization measurements reveal a ferrimagnetic ground state of MnFe3N in the space group P4/mmm
Optimizing configurations for determining the magnetic model based on ab initio calculations
(Cornell University, 2020-06)
In this paper, it is presented a novel strategy to optimize the determination of magnetic couplings by using ab-initio calculations of the energy. This approach allows determining efficiently, in terms of a proposed effective ...
The adsorption of CO on potassium doped molybdenum carbide surface: An ab-initio study
(Elsevier Science, 2012-02)
We have studied the effect of K on the adsorption of CO on the β-Mo 2C (0 0 1) surface with a periodic supercell method using Density Functional Theory calculations, with the PBE functional the generalized gradient ...