The role of convolutional neural networks in scanning probe microscopy: a review

• Ido Azuri,
• Irit Rosenhek-Goldian,
• Neta Regev-Rudzki,
• Georg Fantner and
• Sidney R. Cohen

Beilstein J. Nanotechnol. 2021, 12, 878–901, doi:10.3762/bjnano.12.66

Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization

• Jari Järvi,
• Patrick Rinke and
• Milica Todorović

Beilstein J. Nanotechnol. 2020, 11, 1577–1589, doi:10.3762/bjnano.11.140

• 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

• properties, and ultimately allow us to tune the functionality of advanced materials. Keywords: Bayesian optimization; camphor; Cu(111); density-functional theory; electronic structure; organic surface adsorbates; physical chemistry; structure search; surface science; Introduction Current frontier

• difficult to apply and can lead to biased or incorrect results. For example, with only partial knowledge of the PES, a metastable local minimum energy structure could easily be misinterpreted as the most stable global minimum. Recently, Gaussian processes (GPs) [19] and Bayesian optimization (BO) [20] have