Aug 2026· Materials· Vol 19, pp. 3308· 0 citations· 35 references
Medicine
Abstract
Sodium hydride (NaH) is the main by-product generated during the operation of the cold traps in a sodium-cooled fast reactor. Its thermal decomposition releases hydrogen gas, posing a safety hazard. However, understanding the atomic-scale decomposition mechanism remains challenging because conventional simulation methods are limited in the accessible length and time scales. This study developed a deep neural network potential (DP) for the NaH system using the DP-GEN active learning framework. Benchmark tests show that the DP model accurately reproduces density functional theory (DFT) reference energies, forces, equations of state, elastic properties, and phonon spectra. In particular, the DP-predicted bulk modulus and lattice constant are in good agreement with DFT results and close to experimental values, significantly outperforming the empirical ReaxFF potential. Subsequently, we conducted large-scale Deep Potential Molecular Dynamics (DPMD) simulations to investigate the thermal decomposition behavior of NaH clusters and a slab model. The simulation results reveal model-dependent thermal responses of NaH. In the original Na48H48 cluster simulation heated from 100 to 1200 K, a structural transition and disordering were observed, but no H2 formation occurred within the simulation time. In contrast, the slab model heated from 300 to 1500 K exhibited surface disordering, Na-H bond cleavage, H-H bond formation, cluster detachment, and H2 formation and release at elevated temperatures. A supplementary higher-temperature Na48H48 cluster simulation further showed cluster dissociation in the 1000–1500 K range. These results provide atomistic insight into the model- and temperature-dependent decomposition behavior of NaH and suggest that the DP model is a useful tool for studying hydrogen-related processes in alkali metal hydrides.
Machine learning potential-driven molecular dynamics simulations (ML-MD) were employed to provide atomistic insights into the dehydrogenation kinetics of pristine and doped MgH2. Through systematic investigation of distinct surface orientations, the MgH2 (100) surface was identified as the most active low-index surface for hydrogen release. For pristine MgH2, our simulations revealed a novel H2 formation mechanism characterized by H2 generation in the subsurface region followed by diffusion to the surface for desorption, highlighting the critical role of subsurface processes beyond conventional surface-driven pathways. Comprehensive screening of 22 doping elements identified Ni as the most effective dopant. Among several descriptors, machine learning analysis identified the time-coupled Miedema electron density as the critical descriptor, underscoring the role of electronic properties. Consequently, a volcano-shaped relationship was uncovered between the intrinsic Miedema electron density ( nws ) and total hydrogen release (optimal window: 4.0<nws<5.4x10-2 e/bohr3). Dopants within this range serve a dual function: acting as thermodynamic sinks for H attraction while maintaining a balanced interaction strength to facilitate H-H coupling and H2 release. This atomistic-level validation provides strong theoretical support for the experimentally observed"hydrogen pump"effect of catalytic phases. The present study demonstrates the strong capability of ML- MD in navigating through complex catalytic mechanisms and establishing quantitative property-activity relationships, providing a robust framework for rational design of high-performance catalysts for MgH2 and other hydrogen storage materials.
Bo Han, Jian-Chuan Wang, Rui Zhang et al.· 0 citations
ABSTRACT This study examines the structural stability of CoFe2O4 and the energetics of Langmuir–Hinshelwood recombinative desorption of two pre-adsorbed hydrogen atoms on the CoFe2O4 (111) surface, relevant to high-temperature thermochemical hydrogen production. A pretrained universal M3GNet graph neural-network potential is used for machine-learning molecular dynamics (MLMD), combined with surface DFT calculations at the PBE level. Two-phase MLMD simulations identify an equilibrium melting temperature of approximately 1350 K, while single-phase heating of a defect-free crystal yields an apparent transition at 1700K, interpreted as a superheating-limited upper bound. Within this solid-phase temperature window, DFT calculations show that atomic H is strongly chemisorbed on Co (Eads = −2.04 eV) and O (Eads = −4.29 eV) sites, with O-H bond lengths consistent with experiment, while molecular H₂ is only weakly physisorbed (Eads = 0.001 eV). The recombinative desorption of two co-adsorbed H atoms on adjacent Co/O sites proceeds with an activation barrier of 0.46 eV and an estimated rate of 1.6 × 1010 s−1 at 1300 K, confirming that H-H recombination is fast and is not the rate-limiting step under hydrogen-rich conditions. All DFT values are reported without an explicit Hubbard U correction and represent best feasible estimates within the present computational constraints.
R. Arifin, Y. Winardi, I. Widaningrum et al.· Molecular Simulation· 0 citations
The molten salt reactor (MSR) is gaining increasing prominence in the nuclear power industry. The LiF-BeF2 molten salt system, utilized as a primary coolant and carrier salt in Generation IV nuclear reactors, poses considerable challenges for the experimental measurement of its performance owing to its highly corrosive characteristics and the acute toxicity of beryllium. The deep potential generator (DP-GEN) active learning framework features a fully automatic active learning closed loop, which greatly reduces the computational cost of density functional theory (DFT) calculations. In addition, the DP descriptor exhibits strong universality and is applicable to multi-ion molten salt systems. This work developed a machine learning potential (MLP) for the LiF-BeF2 system using the DP-GEN active learning framework. The potential function demonstrates high accuracy across a wide temperature range and enables a bridge between the structure and macroscopic thermal properties. The results show that the intensity of the first peak of the radial distribution function for Be–F is greater than that of Li–F, indicating that F– forms a stronger bond with Be2+. This made it difficult for F– to gain sufficient energy to escape the Be2+ coordination shell, thereby enabling the formation of a stable [BeF4]2– tetrahedral structure. The density, specific heat capacity, viscosity, and thermal conductivity obtained from the deep potential molecular dynamics simulation are in high agreement with the experimental data, with relative errors of 1.04%, 2.40%, 7.05%, and 1.08%, respectively.
He Tian, Xianyou Lan, Tianyu Liu et al.· ACS Applied Energy Materials· 0 citations
BeF2 is a key component of fluoride coolants in molten salt reactors. The structural phase transition between its α- and β-quartz phases at high temperatures directly affects the mechanical and thermal properties. In this work, by combining first-principles calculations and machine learning molecular dynamics simulations, we systematically investigate the structural stability, elastic properties, and anharmonic lattice dynamics of BeF2 at finite temperatures. A displacive second-order phase transition from the α to the β phase driven by anharmonic effects is revealed. At low temperatures, F atoms are confined in a multi-well potential landscape, and their off-center displacements induce a multi-peak distribution of Be atoms through the Be-F network coupling. As the temperature increases, enhanced anharmonic atomic motion and dynamic averaging gradually smear out the multi-well energy barriers, releasing F atoms from their local confinement and weakening the Be-F vibrational coupling, which ultimately stabilizes the high-symmetry β phase. The high temperature β phase exhibits higher bulk and Young's moduli, indicating enhanced resistance to compression. The imaginary phonon modes present in the harmonic phonon spectrum disappear at high temperatures due to anharmonic renormalization, confirming the dynamical stability of the β phase at elevated temperatures. The temperature evolution of the phonon density of states further shows that the Be-F vibrational coupling weakens with increasing temperature, and the Be-related modes in the high-frequency region undergo red shifts and broadening, consistent with the enhanced anharmonicity and increased local disorder. This study provides a systematic theoretical basis for understanding the microscopic mechanism of the quartz-type phase transition in BeF2 and offers guidance for optimizing the high-temperature service performance of fluoride materials in molten salt reactors.
Meng-Yao Gou, Wen-qian Chen, Wei Li et al.· Journal of Chemical Physics· 0 citations
Polyethylene (PE) is one of the most commonly used synthetic polymers. While the synthesis and processing protocols for PE are well established, precise experimental assignment of microscopic structures at atomistic resolution (i.e., the position of each atom) remains largely limited to highly crystalline systems. This gap is often addressed via computer simulations using empirical interatomic potentials, which use approximate but efficient descriptions of interatomic interactions to reach the length and time scales needed to describe macromolecules. These empirical potentials typically perform well for bulk and/or collective properties but face challenges with chemical realism for complex systems, e.g., during reactive processes. In this work, we address this challenge by combining the computational efficiency of a deep potential (DP) machine-learning force field and the chemical realism of first-principles van der Waals (vdW) corrected hybrid density functional theory (DFT) enabled by a SeA high-throughput framework. Using this approach, we study the structure and dynamics of PE oligomers and polymers in an ethylene solvent under common high-pressure (supercritical) radical polymerization conditions. We found that the local solvation environment of radical-containing PE oligomers converges for chain lengths greater than (n~6), suggesting extensibility of our oligomer-trained MLFF to significantly longer polymers. We then confirmed the extensibility of these models to long PE chains by characterizing the molecular weight scaling of single-chain structure and dynamics, which showed classic good solvent behavior. Our PE MLFF retained a consistent level of fidelity and stability across a wide range of thermodynamic state points and chain lengths, at full atomistic resolution, therefore paving the way towards first-principles-based polymer structure and property prediction.
Bharatha K. Gunawardana, Teresa Shah, B. Azizova et al.· 0 citations
Aqueous lithium-ion batteries provide a promising route toward safe and sustainable energy storage, yet their energy density is constrained by the narrow electrochemical stability window of aqueous electrolytes. Although high salt concentrations can kinetically extend the cathodic limit of the electrolyte via anion-derived interphases, this effect depends strongly on the electrode material and is absent on platinum. To elucidate the interfacial reaction mechanisms behind the absence of cathodic-limit extension on platinum, we performed molecular dynamics simulations based on a machine learning force field, combining near first-principles accuracy with large-scale configurational sampling. The simulations reveal a dominant pathway for anion decomposition on a hydrogen-covered platinum surface under cathodic conditions, mediated by adsorbed hydrogen (H
*
). However, this pathway directly competes with hydrogen evolution via the Tafel step (2H
*
→ H
2
) at comparable activation barriers, suppressing the formation of stable anion-derived interphases and the extension of the cathodic limit. These findings reveal interfacial H
*
coverage as a key factor governing electrode-dependent cathodic stabilization in concentrated aqueous electrolytes.
