Related papers: Combining Hammett $\sigma$ constants for $\Delta$-…
Coulomb collisions in particle simulations for weakly coupled plasmas are modeled by the Landau-Fokker-Planck equation, which is typically solved by Monte-Carlo (MC) methods. One of the main disadvantages of MC is the timestep accuracy…
Powder metallurgy hot isostatic pressing (PM-HIP) is an advanced manufacturing process that produces near net shape parts with high material utilization and uniform microstructures. Despite being used frequently to produce small-scale…
The $\Lambda p$ interaction close to the $\Sigma N$ threshold is considered. Specifically, the pronounced structure seen in production reactions like $K^-d \to \pi^- \Lambda p$ and $pp\to K^+ \Lambda p$ around the $\Sigma N$ threshold is…
The ammonia dimer (NH3)2 has been investigated using high--level ab initio quantum chemistry methods and density functional theory (DFT). The structure and energetics of important isomers is obtained to unprecedented accuracy without…
Spectral observations below Lyman-alpha are now obtained with the Cosmic Origin Spectrograph (COS) on the Hubble Space Telescope (HST). It is therefore necessary to provide an accurate treatment of the blue wing of the Lyman-alpha line that…
The master asymptotic behavior of the usual parachor correlations, expressing surface tension $\sigma$ as a power law of the density difference $\rho_{L}-\rho_{V}$ between coexisting liquid and vapor, is analyzed for a series of pure…
Contrastive Language-Image Pre-training (CLIP) has become the standard for cross-modal image-text representation learning. Improving CLIP typically requires additional data and retraining with new loss functions, but these demands raise…
The transition to a low-carbon economy demands efficient and sustainable energy-storage solutions, with hydrogen emerging as a promising clean-energy carrier and with metal hydrides recognized for their hydrogen-storage capacity. Here, we…
We present a highly efficient molecular dynamics scheme for calculating the concentration profile of dopants implanted in group-IV alloy, and III-V zinc blende structure materials. Our program incorporates methods for reducing computational…
Intuitively, doping represents one of the most promising avenues for optimization of best prospect superconductors (SC) such as conventional high-pressure SCs with record critical temperatures. However, doping at high pressure (HP) is very…
Understanding and accurately predicting hydrogen diffusion in materials is challenging due to the complex interactions between hydrogen defects and the crystal lattice. These interactions span large length and time scales, making them…
A linear regression-based machine learned interatomic potential (MLIP) was developed for the silicon-carbon system. The MLIP was predominantly trained on structures discovered through a genetic algorithm, encompassing the entire…
We report a novel hybrid method of simultaneous atomistic simulation of solids in critical regions (contacts surfaces, cracks areas, etc.), along with continuum modeling of other parts. The continuum is treated in terms of quasi-atoms of…
We propose a method to separate \Delta\eta-dependent and \Delta\eta-independent azimuthal correlations using two- and four-particle cumulants between pseudo-rapidity (\eta) bins in symmetric heavy-ion collisions. The \Delta\eta-independent…
In this paper, we propose an approach to reduce the optimal controller synthesis problem of hybrid systems to quantifier elimination; furthermore, we also show how to combine quantifier elimination with numerical computation in order to…
We report on an effective Hubbard Hamiltonian approach for the study of electronic correlations in C$_{20}$ isomers, cage, bowl and ring, with quantum Monte Carlo and exact diagonalization methods. The tight-binding hopping parameter, $t$,…
The rapid development of computational materials science powered by machine learning (ML) is gradually leading to solutions to several previously intractable scientific problems. One of the most prominent is machine learning interatomic…
Multireference methods such as multiconfiguration pair-density functional theory (MC-PDFT) offer an effective means of capturing electronic correlation in systems with significant multiconfigurational character. However, their application…
Dataset distillation compresses large-scale datasets into compact synthetic sets while preserving training performance, but existing methods are largely restricted to single-modal or bimodal settings. Extending dataset distillation to…
Titanium and its alloys are technologically important materials that display a rich phase behaviour. In order to enable large-scale, realistic modelling of Ti and its alloys on the atomistic scale, Machine Learning Interatomic Potentials…