Järvi Jari, Rinke Patrick, Todorović Milica
Department of Applied Physics, Aalto University, P.O. Box 11100, 00076 Aalto, Espoo, Finland.
Beilstein J Nanotechnol. 2020 Oct 19;11:1577-1589. doi: 10.3762/bjnano.11.140. eCollection 2020.
Identifying the atomic structure of organic-inorganic interfaces is challenging with current research tools. Interpreting the structure of complex molecular adsorbates from microscopy images can be difficult, and using atomistic simulations to find the most stable structures is limited to partial exploration of the potential energy surface due to the high-dimensional phase space. In this study, we present the recently developed Bayesian Optimization Structure Search (BOSS) method as an efficient solution for identifying the structure of non-planar adsorbates. We apply BOSS with density-functional theory simulations to detect the stable adsorbate structures of (1)-camphor on the Cu(111) surface. We identify the optimal structure among eight unique types of stable adsorbates, in which camphor chemisorbs via oxygen (global minimum) or physisorbs via hydrocarbons to the Cu(111) surface. This study demonstrates that new cross-disciplinary tools, such as BOSS, facilitate the description of complex surface structures and their properties, and ultimately allow us to tune the functionality of advanced materials.
利用当前的研究工具来确定有机-无机界面的原子结构具有挑战性。从显微镜图像解释复杂分子吸附物的结构可能很困难，并且由于高维相空间的原因，使用原子模拟来寻找最稳定结构仅限于对势能面的部分探索。在本研究中，我们提出了最近开发的贝叶斯优化结构搜索（BOSS）方法，作为确定非平面吸附物结构的有效解决方案。我们将BOSS与密度泛函理论模拟相结合，以检测(1)-樟脑在Cu(111)表面的稳定吸附物结构。我们在八种独特类型的稳定吸附物中确定了最优结构，其中樟脑通过氧化学吸附（全局最小值）或通过碳氢化合物物理吸附到Cu(111)表面。这项研究表明，诸如BOSS之类的新型跨学科工具有助于描述复杂的表面结构及其性质，并最终使我们能够调整先进材料的功能。
