Related papers: A Gaussian Approximation Potential for Amorphous S…

The marriage of density functional theory (DFT) and deep learning methods has the potential to revolutionize modern computational materials science. Here we develop a deep neural network approach to represent DFT Hamiltonian (DeepH) of…

Defects in crystalline silicon consisting of a silicon self-interstitial atom and one, two, three, or four hydrogen atoms are studied within density-functional theory (DFT). We search for low-energy defects by starting from an ensemble of…

Hydrogenated amorphous silicon (a-Si:H) is a material having an intrinsically high radiation hardness that can be deposited on flexible substrates like Polyimide. For these properties a-Si:H can be used for the production of flexible…

Homogenisation empowers the efficient macroscale system level prediction of physical scenarios with intricate microscale structures. Here we develop an innovative powerful, rigorous and flexible framework for asymptotic homogenisation of…

We present a new efficient way to perform hybrid density functional theory (DFT) based electronic structure calculation. The new method uses an interpolative separable density fitting (ISDF) procedure to construct a set of numerical…

Gaussian Process Regression-based Gaussian Approximation Potential has been used to develop machine-learned interatomic potentials having density-functional accuracy for free sodium clusters. The training data was generated from a large…

While traditional trial-and-error methods for designing amorphous alloys are costly and inefficient, machine learning approaches based solely on composition lack critical atomic structural information. Machine learning interatomic…

The photosensitivity of silicon is inherently very low in the visible electromagnetic spectrum, and it drops rapidly beyond 800 nm in near-infrared wavelengths. Herein, we have experimentally demonstrated a technique utilizing…

The highly anisotropic thermal conductivity in layered materials is crucial for a broad range of applications such as thermal management of electronic devices, thermal insulation, and thermoelectrics. Understanding of anisotropic thermal…

The integration of III-V on silicon is still a hot topic as it will open up a way to co-integrate Si CMOS logic with photonic vices. To reach this aim, several hurdles should be solved, and more particularly the generation of antiphase…

The ultra-long relaxation time of glass transition makes it difficult to construct atomic models of amorphous materials by conventional methods. We propose a novel method for building such atomic models using data assimilation method by…

Generative models show great promise for the inverse design of molecules and inorganic crystals, but remain largely ineffective within more complex structures such as amorphous materials. Here, we present a diffusion model that reliably…

The sample dependence of various properties of hydrogenated amorphous silicon ($a$-Si:H) have been studied with 216 silicon atoms and 24 hydrogen atoms using the density functional based tight binding molecular dynamics simulations. The…

Several amorphous silicon structures were generated using a classical molecular dynamics (MD) protocol of melting and quenching with different quenching rates. An analysis of the calculated electronic properties of these structures revealed…

We introduce GEARS H, a state-of-the-art machine-learning Hamiltonian framework for large-scale electronic structure simulations. Using GEARS H, we present a statistical analysis of the hole concentration induced in defective…

High-throughput DFT calculations are key to screening existing/novel materials, sampling potential energy surfaces, and generating quantum mechanical data for machine learning. By including a fraction of exact exchange (EXX), hybrid…

The HASPIDE (Hydrogenated Amorphous Silicon PIxels DEtectors) project aims at the development of thin hydrogenated amorphous silicon (a-Si:H) detectors on flexible substrates (mostly Polyimide) for beam monitoring, neutron detection and…

We have investigated the response of amorphous silicon (a-Si), in particular crystallization, to external mechanical shear deformations using classical molecular dynamics (MD) simulations and the empirical Environment Dependent Inter-atomic…

A method for performing variable-width (thawed) Gaussian wavepacket (GWP) variational dynamics on machine-learned potentials is presented. Instead of fitting the potential energy surface (PES), the anharmonic correction to the global…

Molecular dynamics simulations using a Gaussian Approximation Potential (GAP) reveal a stress triaxiality driven, two-step Si-I (diamond cubic) to Si-V (simple hexagonal) phase transition pathway in a spherical Si nanoparticle with a 10 nm…