AI 中文总结
研究利用机器学习势驱动分子动力学模拟，探究原始和掺杂MgH₂脱氢动力学，确定MgH₂（100）表面最活跃，发现新H₂形成机制，筛选出有效掺杂剂Ni，揭示相关关系，为高性能催化剂设计提供框架。
AI 中文摘要
采用机器学习势驱动的分子动力学模拟（ML-MD）来深入了解原始和掺杂MgH₂的脱氢动力学。通过系统研究不同表面取向，发现MgH₂（100）表面是最活跃的低指数氢释放表面。对于原始MgH₂，揭示了一种新的H₂形成机制，即H₂在次表面区域生成后扩散到表面解吸，凸显了次表面过程的关键作用。综合筛选22种掺杂元素，确定Ni是最有效的掺杂剂。机器学习分析确定时间耦合的Miedema电子密度是关键描述符。发现本征Miedema电子密度（nws）与总氢释放之间存在火山形关系（最佳窗口：4.0 < nws < 5.4x10⁻² e/bohr³）。该范围内的掺杂剂具有双重作用。本研究证明了ML-MD在探索复杂催化机制和建立定量性质-活性关系方面的强大能力，为合理设计MgH₂和其他储氢材料的高性能催化剂提供了有力框架。
英文摘要
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.
