Materials Interaction with Femtosecond Lasers

Bauerhenne, Bernd

doi:10.1007/978-3-030-85135-4

Cited by 4 publications

(3 citation statements)

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“…Finally, we calculate the melting temperature of Si by simulating the coexistence of liquid and crystal using a simulation setup described in Ref. [30]. We determine the melting temperature…”

Section: Neural Network Potential For Laser-excited Siliconmentioning

confidence: 99%

“…Furthermore, the electron-phonon coupling is included by performing a velocity scaling. However, since we are using a T edependent interatomic potential to also take non-thermal effects into account, we deploy the extended MD simulation setup introduced by Bauerhenne [30],…”

Section: Details On Dft Calculations and MD Simulationsmentioning

confidence: 99%

“…On longer timescales, however, the T e -dependency of the interatomic potential becomes even more important because the electronic temperature cannot be assumed to be constant anymore due to the electron-phonon coupling. Only with T edependent interatomic potentials it can be possible to perform large-and ultralarge-scale MD simulations treating laser-induced bond changes and incoherent lattice heating via electron-phonon coupling on the same accuracy level [30]. Further utilizing the benefits of machine learning potentials could provide insights on interesting long-term effects like the spontaneous formation of laser-induced periodic surface structures [31][32][33], which is still not fully understood.…”

Section: Introductionmentioning

confidence: 99%

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Neural Network Interatomic Potential for Laser-Excited Materials

Plettenberg,

Bauerhenne,

Garcia

2023

Preprint

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The design of new materials and the prediction of their properties by means of computational techniques has reached an unprecedent level of accuracy thanks to the development and use of machine-learning (ML) approaches. For instance, interatomic potentials based on ML algorithms have been increasingly used to perform large-scale, first-principles quality simulations of materials in the electronic ground state. However, they are unable to describe situations in which the electrons are excited, like in the case of material processing by means of femtosecond laser irradiation or ion bombardment. In this work, we propose a neural network interatomic potential, which involves a dependency on the electronic temperature and can therefore be applied to large-scale simulations of laser-excited materials. We explicitly construct the interatomic potential for silicon and demonstrate its capability of reproducing important physical properties with first-principles accuracy. Moreover, we use it to simulate laser-induced surface modifications on a silicon thin film and thereby consider time and length scales that are not feasible with first-principles approaches like density functional theory. The method is general and can be applied not only to other laser-excited materials but also to condensed and liquid matter under non-equilibrium situations in which electrons and ions exhibit different temperatures.

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Section: Neural Network Potential For Laser-excited Siliconmentioning

confidence: 99%

Section: Details On Dft Calculations and MD Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neural Network Interatomic Potential for Laser-Excited Materials

Plettenberg,

Bauerhenne,

Garcia

2023

Preprint

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Interatomic Potential For Carbon Based Quantum‐Technology Applications

Xibraku,

Garcia,

Bauerhenne

2024

Adv Quantum Tech

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To optimize parameters for laser processing of quantum‐technology relevant materials, such as diamond, precise atomistic simulations of the light‐matter interaction on large scales (on the order of atoms) are essential. Classical empirical interatomic potentials are commonly employed for simulating such a large number of atoms, however they fail to accurately capture all relevant effects of light‐matter interaction. Conversely, ab initio methods like Density Functional Theory (DFT) can effectively incorporate quantum properties arising from photon excitations, but their applicability is limited to small systems containing at most approximately atoms. Consequently, bridging the gap between achieving DFT precision and handling millions of atoms necessitates the development of innovative classes of interatomic potentials. In this paper, the construction of a highly accurate interatomic potential for diamond is presented, derived from an extensive dataset of DFT calculations. The parameters of the interatomic potential depend on the electronic temperature (). The findings demonstrate that this newly developed interatomic potential can aptly describe the laser processing of diamond for nanophotonic applications, achieving accuracy comparable to ab initio methods for large systems.

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