Martin Matas | Searcharxiv

arXiv2026

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

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.

cond-mat.mtrl-sci↗

arXiv2024

A Predictive Model for Catalytic Methane Pyrolysis

Methane pyrolysis provides a scalable alternative to conventional hydrogen production methods, avoiding greenhouse gas emissions. However, high operating temperatures limit economic feasibility on an industrial scale. A major scientific goal is, therefore, to find a catalyst material that lowers operating temperatures, making methane pyrolysis economically viable. In this work, we derive a model that provides a qualitative comparison of possible catalyst materials. The model is based on calculations of adsorption energies using density functional theory. Thirty different elements were considered. Adsorption energies of intermediate molecules in the methane pyrolysis reaction correlate linearly with the adsorption energy of carbon. Moreover, the adsorption energy increases in magnitude with decreasing group number in the $d$-block of the periodic table. For a temperature range between 600 and 1200 K and a normalized partial pressure range for $\mathrm{H_2}$ between $10^{-1}$ and $10^{-5}$, a total of eighteen different materials were found to be optimal catalysts at least once. This indicates that catalyst selection and reactor operating conditions should be well-matched. The present work establishes the foundation for future large-scale studies of surfaces, alloy compositions, and material classes using machine learning algorithms.

cond-mat.mtrl-sci↗