Related papers: Self-learning analytical interatomic potential des…
Machine learning interatomic potentials (MLIPs) have become increasingly effective at approximating quantum mechanical calculations at a fraction of the computational cost. However, lower errors on held out test sets do not always translate…
Constructing an accurate atomistic model for the high-pressure phases of tin (Sn) is challenging because properties of Sn are sensitive to pressures. We develop machine-learning-based deep potentials for Sn with pressures ranging from 0 to…
Machine learning interatomic potentials (MLIPs) offer an efficient and accurate framework for large-scale molecular dynamics (MD) simulations, effectively bridging the gap between classical force fields and \textit{ab initio} methods. In…
Looking back at seven decades of highly extensive application in the semiconductor industry, silicon and its native oxide SiO$_2$ are still at the heart of several technological developments. Recently, the fabrication of ultra-thin oxide…
The effective potential of scalar quantum electrodynamics with N flavors of complex scalar fields is studied, by performing a self consistent 1/N expansion up to next to leading order in $1/N$. Starting from the broken phase at zero…
Molecular dynamics simulations have been extensively used to predict thermal properties, but simulating different phases with similar precision using a unified force field is often difficult, due to the lack of accurate and transferrable…
We investigate the feasability of improving the semi-empirical density functional based tight-binding method (DFTB) through a general and transferable many-body repulsive potential for pure silicon using a common machine-learning framework.…
Self-heating (SHE) TCAD numerical simulations have been performed, for the first time, on 30nm FDSOI MOS transistors at extremely low temperatures. The self-heating temperature rise dTmax and the thermal resistance Rth are computed as…
The microstructure of the Ti-Al binary system is an area of great interest as it affects material properties and plasticity. Phase transformations induce microstructural changes; therefore, accurately modeling the phase transformations of…
We calculate the dielectric response of crystalline silicon following irradiation by a high-intensity laser pulse, modeling the dynamics by time-dependent density functional theory (TDDFT). The pump-probe measurements are numerically…
Machine learning interatomic potentials (MLIPs) based on a large dataset obtained by density functional theory (DFT) calculation have been developed recently. This study gives both conceptual and practical bases for the high accuracy of…
We generalize the concept of conserving,\Phi-derivable, approximations to relativistic field theories. Treating the interaction field as a dynamical degree of freedom, we derive the thermodynamical potential in terms of fully dressed…
We study the applicability of the finite temperature effective potential in the equation of motion of a homogeneous "misaligned" scalar condensate $\varphi$, and find important caveats that severely restrict its domain of validity: i:) the…
In this work, we propose a linear machine learning force matching approach that can directly extract pair atomic interactions from ab initio calculations in amorphous structures. The local feature representation is specifically chosen to…
Hydrogenation of amorphous silicon (a-Si:H) is critical for reducing defect densities, passivating mid-gap states and surfaces, and improving photoconductivity in silicon-based electro-optical devices. Modelling the atomic scale structure…
Laser excitation in silicon from femto- to pico-second time scales is studied. We assume the Three-Temperature Model (3TM) which describes the dynamics of the distinct quasi-temperatures for electrons, holes, and lattice. Numerical results…
The inherent network of nanopores and voids in silicon dioxide (SiO2) is generally undesirable for aspects of film quality, electrical insulation and dielectric performance. However, if we view these pores as natural nano-patterns embedded…
We study the effective potential of a real scalar phi^4 theory as a function of the temperature T within the simplest Phi-derivable approximation, namely the Hartree approximation. We apply renormalization at a "high" temperature T* where…
Si and its oxides have been extensively explored in theoretical research due to their technological and industrial importance. Simultaneously describing interatomic interactions within both Si and SiO$_2$ without the use of \textit{ab…
Accurate atomistic simulations of gas-surface scattering require potential energy surfaces that remain reliable over broad configurational and energetic ranges while retaining the efficiency needed for extensive trajectory sampling. Here,…