Related papers: Atomic cluster expansion potential for the Si-H sy…
We present a new interatomic potential for water captured in a charge-transfer embedded atom method (EAM) framework. The potential accounts for explicit, dynamical charge transfer in atoms as a function of the local chemical environment. As…
In this work we investigate small clusters of helium atoms using the hyperspherical harmonic basis. We consider systems with $A=2,3,4,5,6$ atoms with an inter-particle potential which does not present a strong repulsion at short distances.…
We propose a method to simulate a-Si and a-Si:H using an ab initio approach based on the Harris functional and thermally amorphisized periodically continued cells with at least 64 atoms, and calculate their radial distribution functions.…
Semi-empirical interatomic potentials have been developed for Al, alpha-Ti, and gamma-TiAl within the embedded atomic method (EAM) by fitting to a large database of experimental as well as ab-initio data. The ab-initio calculations were…
Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of…
As an anode material for lithium-ion batteries, amorphous silicon offers a significantly higher energy density than the graphite anodes currently used. Alloying reactions of lithium and silicon, however, induce large deformation and lead to…
An approximate but straight forward projection method to molecular many alpha-particle states is proposed and the overlap to the shell model space is determined. The resulting space is in accordance with the shell model, but still contains…
We present a systematic coarse-graining (CG) strategy for many particle molecular systems based on cluster expansion techniques. We construct a hierarchy of coarse-grained Hamiltonians with interaction potentials consisting of two, three…
Next-generation lithium-ion batteries with silicon anodes have positive characteristics due to higher energy densities compared to state-of-the-art graphite anodes. However, the large volume expansion of silicon anodes can cause high…
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…
Machine learning potentials (MLPs) are becoming powerful tools for performing accurate atomistic simulations and crystal structure optimizations. An approach to developing MLPs employs a systematic set of polynomial invariants including…
Ab initio molecular dynamics simulations are used to investigate the structure and electronic properties of the liquid Ag-Se system at three compositions. The realism of the simulations is demonstrated by comparison with diffraction data…
Using a combination of numerical simulations and atom-probe tomography experiments, we determine the interfacial energy of Cu nanocrystals precipitated within the amorphous matrix of FINEMET (molar composition Fe72.89Si16.21B6.90Nb3Cu1).…
The success of first principles electronic structure calculation for predictive modeling in chemistry, solid state physics, and materials science is constrained by the limitations on simulated length and time scales due to computational…
The electronic excitation occurring on adsorbates at ultrafast time scales from optical lasers that initiate surface chemical reactions is still an open question. Here, we report the ultrafast temporal evolution of X-ray absorption…
We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating…
The outstanding demonstration of quantum confinement in Si nanocrystals (Si NC) in a SiC matrix requires the fabrication of Si NC with a narrow size distribution. It is understood without controversy that this fabrication is a difficult…
Data-driven interatomic potentials have emerged as a powerful class of surrogate models for {\it ab initio} potential energy surfaces that are able to reliably predict macroscopic properties with experimental accuracy. In generating…
Energy functions for pure and heterogenous systems are one of the backbones for molecular simulation of condensed phase systems. With the advent of machine learned potential energy surfaces (ML-PESs) a new era has started. Statistical…
Dye-sensitized solar cells (DSC), built upon organic/inorganic hybrid materials, are promising third generation solar cells, which attract wide attentions due to their low-cost, flexibility and environment-friendly properties. We review…