石墨炔原子催化剂的崭新道路:基于自验证机器学习方法的筛选策略

Rational Design of Graphdiyne-based Atomic Electrocatalysts:DFT and Self-validated Machine Learning

近年来,原子催化剂(ACs)引起了广泛的研究关注.目前该领域的长足发展受限于贵金属的使用和单原子催化剂(SACs)的性能有限.本文总结了利用密度泛函理论(DFT)和机器学习(ML)方法筛选高效的基于石墨炔(GDY)的原子催化剂的工作.研究表明, Pd, Co, Pt和Hg可以形成稳定的零价过渡金属-石墨炔组合(TM-GDY),而镧系-过渡金属的双原子催化剂(Ln-TM DAC)组合通过f-d轨道耦合作用可以获得有效的催化性能提升.进一步分析表明,主族元素与过渡金属和镧系金属的结合可以通过p轨道耦合保持高电活性,从而构成高度稳定的GDY-DAC系统,机器学习算法也揭示了s,p轨道的作用.此外,理论算法技术在筛选催化水分解析氢反应(HER)的高效组合上也表现出了优越性,创新性地预测了石墨炔-原子催化剂在实际催化反应中的潜能.本综合评述可为未来设计新型原子催化剂提供新的思路与策略.

Although atomic catalysts(ACs)have attracted intensive attention in recent years,the current progress of this area is limited by the use of noble metal as well as single atomic catalysts(SACs). Here,we summarize the recent works in screening highly-efficient graphdiyne-ACs(GDY-ACs)with the utilization of density functional theory(DFT)calculations and machine learning(ML). Our studies showed that the Pd,Co,Pt and Hg could form stable zero-valence transition metal-GDY(TM-GDY),whereas the lanthanide-TM DAC(Ln-TM DAC) systems were also demonstrated as the promising electrocatalyst candidates because of their long-range site-to-site f-d orbital interactions. The further analysis revealed that the combination of main group elements with TM and Ln metals can achieve high stable GDY-DAC and preserve the high electroactivity due to the long-range p-orbital coupling,while the role of the s-and p-orbitals was studied via ML algorithm. In addition,the DFT calculation and ML techniques also showed great potential in screening possible GDY-based ACs with excellent hydrogen evolution reaction(HER)performances,and the potential of rare-earth-based GDY-ACs for HER has been predicted for the first time. This review has supplied an advanced strategy for future exploration of atomic catalyst.