论文标题
通过电荷传输和催化剂位点的不成对电子的相互调制，以减少N2的出色固有活性：从高通量计算中，有助于机器学习的角度
Mutual modulation via charge transfer and unpaired electrons of catalyt-ic site for superior intrinsic activity of N2 reduction: from high-throughput computations assisted with machine learning perspective
论文作者
论文摘要
氮还原反应（NRR）的电催化剂由于其应用于化石燃料的可再生能源替代品而引起了不断增长的关注。但是，惰性N-N键的激活需要多次复杂的电荷注入，这使催化剂的设计变得复杂。在这里，通过结合原子尺度筛选和机器学习（ML）方法，我们探讨了二硫化钼（MOS2）支持的NRR单原子催化剂（SAC）的合理设计。我们的工作表明，NRR SAC的活性高度依赖于TM的未配对D电子的数量：高活性有利于较高值的阳性样品，而在较低值下分布的负案例则与宿主的掺杂条件有所不同。我们发现，用硼取代硫可以激活某些TM（例如Ti和V）的内在NRR活性，而Ti和V否则原始MOS2上方无活性。重要的是，在ML中使用的各种DE脚本中，吸附的TM的带电状态在通过反捐赠机制将电子捐赠给N2的Pi-Anti-Bonding轨道方面起着关键作用。我们的工作显示了一种可行的NRR SAC合理设计策略，并检索了活性催化剂的决定性特征。
Electrocatalysts of nitrogen reduction reaction (NRR) have attracted ever-growing attention due to its application for renewable energy alternative to fossil fuels. However, activation of inert N-N bond requires multiple complex charge injection which complicates the design of the catalysts. Here via combining atomic-scale screening and machine learning (ML) methods we explore the rational design of NRR single-atom catalysts (SACs) supported by molybdenum disulfide (MoS2). Our work reveals that the activity of NRR SACs is highly dependent on the number of unpaired d electrons of TM: positive samples with high activity favoring higher values while negative cases distributing at lower values, both varying with the doping conditions of the host. We find that the substitution of sulfur with boron can activate the intrinsic NRR activity of some TMs such as Ti and V which are otherwise inactive above pristine MoS2. Importantly, among the various de-scriptors used in ML, the charged state of adsorbed TM plays a key role in donating electron to pi-anti-bonding orbital of N2 via the back-donation mechanism. Our work shows a feasible strategy for rational design of NRR SACs and retrieval of the decisive feature of active catalysts.
