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 (1S)-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.

We have studied the properties of the prototype hybrid organic-inorganic perovskite CH 3 NH 3 PbI 3 using relativistic density functional theory (DFT). For our analysis we introduce the concept of CH 3 NH + 3 "pair modes", that is, characteristic relative orientations of two neighboring CH 3 NH + 3 cations. In our previous work [Phys. Rev. B 94, 045201 (2016)] we identified two preferential orientations that a single CH 3 NH + 3 cation adopts in a unit cell. The total number of relevant pairs can be reduced from the resulting 196 combinations to only 25 by applying symmetry operations. DFT results of several 2×2×2 supercell models reveal the dependence of the total energy, band gap and band structure on the distribution of CH 3 NH + 3 cations and the pair modes. We have then analyzed the pair-mode distribution of a series of 4×4×4 supercell models with disordered CH 3 NH + 3 cations. Our results show that diagonally-oriented CH 3 NH + 3 cations are rare in optimized CH 3 NH 3 PbI 3 supercell structures. In the prevailing pair modes, the C-N bonds of the two neighboring CH 3 NH + 3 cations are aligned approximately vertically. Furthermore, we fit the coefficients of a pair-mode expansion to our supercell DFT reference structures. The pair-mode model can then be used to quickly estimate the energies of disordered perovskite structures. Our pair-mode concept provides combined atomistic-statistical insight into disordered structures in bulk hybrid perovskite materials.

Controlling the properties of organic/inorganic materials requires detailed knowledge of their molecular adsorption geometries. This is often unattainable, even with current state‐of‐the‐art tools. Visualizing the structure of complex non‐planar adsorbates with atomic force microscopy (AFM) is challenging, and identifying it computationally is intractable with conventional structure search. In this fresh approach, cross‐disciplinary tools are integrated for a robust and automated identification of 3D adsorbate configurations. Bayesian optimization is employed with first‐principles simulations for accurate and unbiased structure inference of multiple adsorbates. The corresponding AFM simulations then allow fingerprinting adsorbate structures that appear in AFM experimental images. In the instance of bulky (1S)‐camphor adsorbed on the Cu(111) surface, three matching AFM image contrasts are found, which allow correlating experimental image features to distinct cases of molecular adsorption.