Related papers: Machine Learning Potentials for Hydrogen Absorptio…
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…
Recent machine learning models to accurately obtain gas adsorption isotherms focus on polymers or metal-organic frameworks (MOFs) separately. The difficulty in creating a unified model that can predict the adsorption trends in both types of…
Transition metal chromophores with earth-abundant transition metals are an important design target for their applications in lighting and non-toxic bioimaging, but their design is challenged by the scarcity of complexes that simultaneously…
Silicon carbide (SiC) has long been a subject of study for its application in harsh environments. Existing empirical interatomic potentials for 3C-SiC show significant discrepancies in predicting the properties that are crucial in…
High-entropy MBenes (HE-MBenes) represent a promising, unexplored class of 2D materials for electrocatalysis. In this work, we present a systematic computational screening of 56 equiatomic quinary HE-MBene compositions from the {Ti, V, Cr,…
Evolutionary algorithms and the particle swarm optimization method have been used to predict stable and metastable high hydrides of iron between 150-300 GPa that have not been discussed in previous studies. Cmca FeH5, Pmma FeH6 and P2/c…
Machine Learning Interatomic Potentials (MLIPs) are a modern computational method that allows achieving near-quantum mechanical accuracy (DFT) while still describing large-scale systems in molecular dynamics (MD) simulations. In this work,…
Simulations predict that circumgalactic hydrogen gas surrounding massive ($M_{\rm{halo}}^{z=1}=10^{12}-10^{13}\ M_{\odot}$) galaxies at $z\sim4$ may be predominantly neutral, and could produce damped Ly$\alpha$ absorbers (DLAs) along…
In this work, we have studied the hydrogen adsorption-desorption properties and storage capacities of Li functionalized [2,2,2]paracyclophane (PCP222) using dispersion-corrected density functional theory and molecular dynamic simulation.…
Machine learning interatomic potentials (MLIPs) balance high accuracy and lower costs compared to density functional theory calculations, but their performance often depends on the size and diversity of training datasets. Large datasets…
Atomistic simulations of electrochemical interfaces remain challenging due to the long time scales required to adequately sample the structure of the electric double layer. The emergence of efficient, short-range machine learning…
A machine-learned interatomic potential (MLIP) for multilayer MoS2 was developed using the ultra-fast force field (UF3) framework. The UF3 MLIP reproduces key properties in strong agreement with DFT including lattice constants, interlayer…
In the first phase of LHC data-taking ATLAS will measure the charged-particle density at the initial center-of-mass energy of 10 TeV and then at 14 TeV. This will allow us improve our knowledge of soft QCD models and pin-down cross-sections…
We created a computational workflow to analyze the potential energy surface (PES) of materials using machine-learned interatomic potentials in conjunction with the minima hopping algorithm. We demonstrate this method by producing a…
A temperature-pressure phase diagram has been generated through the study of hydrogen adsorption on the (N_4 C_3 H)_6Li_6 cluster at the B3LYP/6-31G(d) level of theory. Possibility of hydrogen storage in an associated 3D functional material…
Li$_{10}$Ge(PS$_6$)$_2$ (LGPS) is a highly concentrated solid electrolyte, in which Coulombic repulsion between neighboring cations is hypothesized as the underlying reason for concerted ion hopping, a mechanism common among superionic…
Simulating water from first principles remains a significant computational challenge due to the slow dynamics of the underlying system. Although machine-learned interatomic potentials (MLPs) can accelerate these simulations, they often fail…
Unlocking the design of Li-S batteries where no shuttle effects appears, and thus their energy storage capacity does not diminish over time, would enable the manufacturing of energy storage devices more performant than the current Li-ion…
Developing efficient hydrogen storage and conversion technologies is essential for sustainable energy. This study investigates the catalytic potential of a dicalcium nitride (Ca2N) monolayer for hydrogen dissociation using density…
Using new HST/FOS, NTT/EMMI, and Keck/HIRES spectra of the gravitationally-lensed double QSO HE 1104-1805 AB (z=2.31, \theta=3.2), and assuming UV photoionization by a metagalactic radiation field, we derive physical conditions (ionization…