Related papers: Machine Learning Potentials for Hydrogen Absorptio…

The reactivity of silicates in an aqueous solution is relevant to various chemistries ranging from silicate minerals in geology, to the C-S-H phase in cement, nanoporous zeolite catalysts, or highly porous precipitated silica. While…

Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex aqueous systems such as solid-liquid interfaces. Here, we present a machine learning…

Hydrogen segregation to vacancies in the surface and subsurface layers of (111) and (100) surfaces of Pd is studied in the density functional theory (DFT) approach. Adsorption energies and configurations of various clusters of H atoms at…

Machine learning interatomic potentials (MLIPs) have become widely used tools in atomistic simulations. For much of the history of this field, the most commonly employed architectures were based on short-ranged atomic energy contributions,…

Machine learning offers an unprecedented perspective for the problem of classifying phases in condensed matter physics. We employ neural-network machine learning techniques to distinguish finite-temperature phases of the strongly correlated…

As the simplest element in nature, unraveling the phase diagram of hydrogen is a primary task for condensed matter physics. As conjectured many decades ago, in the low-temperature and high-pressure part of the phase diagram, solid hydrogen…

Machine learning interatomic potentials (MLIPs) offer near-ab initio accuracy with the efficiency of classical force fields, making them attractive for modeling electrolytes. Collecting a diverse training set is essential for their accuracy…

One of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with minimal empirical information. First-principles approaches such as the density functional theory (DFT)…

We report the detection of H2 in a zabs= 0.0963 Damped Lyman-{\alpha} (DLA) system towards zem = 0.4716 QSO J1619+3342. This DLA has log N(H I) = 20.55 (0.10), 18.13 < log N(H2) < 18.40, [S/H] = -0.62 (0.13), [Fe/S] = -1.00 (0.17) and the…

Titanium borates show promissing hydrogen storage characteristics. Structural relaxation around individual hydrogen atoms and the binding energies are studied by means of the density functional theory methods for a number of hydrogenated…

Silicon carbide (SiC) divacancies are attractive candidates for spin defect qubits possessing long coherence times and optical addressability. The high activation barriers associated with SiC defect formation and motion pose challenges for…

Adsorption in nanoporous materials is one strategy that can be used to store hydrogen at conditions of temperature and pressure that are economically viable. Adsorption capacity of nanoporous materials depends on surface area which can be…

Understanding the mechanisms of hydrogen embrittlement (HE) is essential for advancing next-generation high-strength steels, thereby motivating the development of highly accurate machine-learning interatomic potentials (MLIPs) for the Fe-H…

Large density functional theory (DFT) databases are a treasure trove of energies, forces and stresses that can be used to train machine learned interatomic potentials for atomistic modeling. Herein, we employ structural relaxations from the…

We report the detection of 21-cm and H2 absorption lines in the same DLA system (log N(HI)=21.36+-0.10) at zabs=3.17447 towards SDSSJ133724+315254 (z=3.174). We estimate the spin temperature of the gas to be, Ts~600 K, intermediate between…

The existence of intrinsic vacancies in cubic (monoclinic) TiO suggests opportunity for hydrogen absorption, which was addressed in recent experiments. In the present work, based on first principle calculations, the preferences are studied…

The conversion of $\mathrm{CO_2}$ to value-added compounds is an important part of the effort to store and reuse atmospheric $\mathrm{CO_2}$ emissions. Here we focus on $\mathrm{CO_2}$ hydrogenation over so-called inverse catalysts:…

We introduce a local machine-learning method for predicting the electron densities of periodic systems. The framework is based on a numerical, atom-centred auxiliary basis, which enables an accurate expansion of the all-electron density in…

The structural, vibrational, energetic and electronic properties of hydrogen at the stoichiometric RuO2(110) termination are studied using density functional theory. The oxide surface is found to stabilize both molecular and dissociated H2.…

Machine-learned potentials (MLPs) trained on ab initio data combine the computational efficiency of classical interatomic potentials with the accuracy and generality of the first-principles method used in the creation of the respective…