Machine Learning Potential-Driven Molecular Dynamics Simulations of Dehydrogenation in Pristine and Doped MgH$_2$

Machine-learning molecular dynamics and DFT analysis of CoFe2O4 spinel stability and hydrogen recombination energetics for high-temperature thermochemical hydrogen production

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.

Molecular Dynamics Study of Hydrogen Release from NaH Using Machine Learning Potential

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-guided discovery of γ-Al2O3 as defect-rich structural ensemble governs single-atom Pt catalysis

Machine-learning-driven investigation of dopant-induced modulation of electronic properties in two-dimensional SnO

Machine learning has become an effective tool for accelerating materials discovery by predicting material properties with far lower computational cost than exhaustive first-principles calculations. However, its application to dopant engineering in two-dimensional (2D) oxide semiconductors remains relatively unexplored. In this work, we combine supervised machine learning with density functional theory-derived data from the Materials Project database to predict the electronic, thermodynamic, transport, and optical properties of undoped and doped 2D tin monoxide (SnO). Several supervised regression algorithms are evaluated for predicting the band gap, formation energy, carrier mobility, electrical conductivity, and optical absorption coefficient. Among the models considered, gradient boosting regression provides the highest predictive accuracy and consistently reproduces the complex structure–property relationships of both undoped and doped systems. Feature-importance analysis reveals that electronic band descriptors, particularly band width, followed by band asymmetry and electronic anisotropy, are the primary factors governing band-gap prediction, while structural descriptors provide complementary contributions. The predictions indicate that monolayer SnO has a wider band gap, slightly higher formation energy, and higher carrier mobility than bulk SnO due to quantum confinement. Mg and Zn substitution allow systematic tuning of the material properties over a moderate concentration range. The band gap changes only slightly with doping, while Mg incorporation is thermodynamically more favorable than Zn, whose stability decreases with increasing concentration. Dopant-induced impurity scattering reduces carrier mobility and electrical conductivity, whereas optical absorption is enhanced in both Mg- and Zn-doped systems. These results demonstrate that machine learning can efficiently identify promising dopant configurations while providing reliable predictions of dopant-dependent material properties. The proposed framework offers a practical approach for the computational design of doped 2D oxide semiconductors for future optoelectronic and sensing applications.

Toward accelerated electrocatalyst design: synergistic integration of DFT, machine learning, and microkinetic modeling.

Emerging opportunities in physics-informed machine learning, graph neural networks, generative artificial intelligence, active learning, and autonomous closed-loop DFT-ML-MKM workflows are discussed as promising directions for accelerating the discovery of next-generation electrocatalysts with enhanced activity, selectivity, and long-term stability.

From Migration-Healing Reconstruction to Chemical Segregation: Atomistic Origins of High-Temperature Evolution in Cubic Boron Nitride Nanoparticles Revealed by Machine Learning Potential Simulations.

Cubic boron nitride (c-BN) nanoparticles are promising for extreme-condition applications, yet their atomistic evolution remains poorly understood. Here, we develop a high-fidelity machine learning potential and perform large-scale deep potential molecular dynamics simulations to investigate their high-temperature behavior. A universal reconstruction pathway is revealed, involving defect formation, inward-to-outward atomic migration, and progressive healing into multilayer hexagonal BN (h-BN). This mechanism is validated across multiple morphologies and exposed facets and is found to be strongly dependent on facet and termination. Furthermore, temperature-programmed dynamics identify ∼1800 K as the critical threshold for activating large-scale atomic flux, driving the transformation from core-shell architectures to multishell fullerene-like h-BN structures. At extreme temperatures (>3300 K), chemical segregation emerges, leading to the formation of boron clusters and polynitrogen chains, consistent with experimental observations. We further compared the reconstruction behaviors of isoelectronic nanodiamond and c-BN nanoparticles, revealing that c-BN exhibits superior thermal stability and enhanced self-healing capability, originating from the higher kinetic barriers associated with partially ionic B-N bonds relative to covalent C-C bonds.