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.

Supporting Information

Supporting information features camphor geometry in global minimum conformer search, convergence of the 6D surrogate model, and coordinates of camphor in the predicted and relaxed stable structures.

Supporting Information File 1: Camphor global minimum conformer, convergence of the 6D model, and coordinates of camphor.
Format: PDF Size: 1.2 MB Download

Cite the Following Article

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.

How to Cite

Järvi, J.; Rinke, P.; Todorović, M. Beilstein J. Nanotechnol. 2020, 11, 1577–1589. doi:10.3762/bjnano.11.140

Download Citation

Citation data can be downloaded as file using the "Download" button or used for copy/paste from the text window below.
Citation data in RIS format can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Zotero.

Presentation Graphic

Picture with graphical abstract, title and authors for social media postings and presentations.
Format: PNG Size: 8.7 MB Download

Citations to This Article

Up to 20 of the most recent references are displayed here.

Scholarly Works

  • Jestilä, J. S.; Wu, N.; Priante, F.; Foster, A. S. Accelerated Lignocellulosic Molecule Adsorption Structure Determination. Journal of chemical theory and computation 2024, 20, 2297–2312. doi:10.1021/acs.jctc.3c01292
  • Baird, S. G.; Hall, J. R.; Sparks, T. D. Compactness matters: Improving Bayesian optimization efficiency of materials formulations through invariant search spaces. Computational Materials Science 2023, 224, 112134. doi:10.1016/j.commatsci.2023.112134
  • Fang, L.; Guo, X.; Todorović, M.; Rinke, P.; Chen, X. Exploring the Conformers of an Organic Molecule on a Metal Cluster with Bayesian Optimization. Journal of chemical information and modeling 2023, 63, 745–752. doi:10.1021/acs.jcim.2c01120
  • Löfgren, J.; Tarasov, D.; Koitto, T.; Rinke, P.; Balakshin, M.; Todorović, M. Machine Learning Optimization of Lignin Properties in Green Biorefineries. ACS Sustainable Chemistry & Engineering 2022, 10, 9469–9479. doi:10.1021/acssuschemeng.2c01895
  • Järvi, J.; Todorović, M.; Rinke, P. Efficient modeling of organic adsorbates on oxygen-intercalated graphene on Ir(111). Physical Review B 2022, 105. doi:10.1103/physrevb.105.195304
  • Fangnon, A.; Dvorak, M.; Havu, V.; Todorović, M.; Li, J.; Rinke, P. Protective Coating Interfaces for Perovskite Solar Cell Materials: A First-Principles Study. ACS applied materials & interfaces 2022, 14, 12758–12765. doi:10.1021/acsami.1c21785
  • Jin, S.-A.; Kämäräinen, T.; Rinke, P.; Rojas, O. J.; Todorović, M. Machine learning as a tool to engineer microstructures: Morphological prediction of tannin-based colloids using Bayesian surrogate models. MRS bulletin 2022, 47, 29–37. doi:10.1557/s43577-021-00183-4
  • Witt, C.; Schmidt, M.-C.; Schröder, C.; Schauermann, S.; Hartke, B. Disordered Two-Dimensional Self-Organization of Ethyl Pyruvate Molecules on the Pt(111) Surface. The Journal of Physical Chemistry C 2021, 125, 26167–26179. doi:10.1021/acs.jpcc.1c07058
  • Maier, S.; Stöhr, M. Molecular assemblies on surfaces: Towards physical and electronic decoupling of organic molecules. Beilstein journal of nanotechnology 2021, 12, 950–956. doi:10.3762/bjnano.12.71
  • Kumar, A.; Banerjee, K.; Ervasti, M. M.; Kezilebieke, S.; Dvorak, M.; Rinke, P.; Harju, A.; Liljeroth, P. Electronic Characterization of a Charge-Transfer Complex Monolayer on Graphene. ACS nano 2021, 15, 9945–9954. doi:10.1021/acsnano.1c01430
  • Arrigoni, M.; Madsen, G. K. H. Evolutionary computing and machine learning for discovering of low-energy defect configurations. npj Computational Materials 2021, 7, 1–13. doi:10.1038/s41524-021-00537-1
  • Järvi, J.; Alldritt, B.; Krejčí, O.; Todorović, M.; Liljeroth, P.; Rinke, P. Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations. Advanced Functional Materials 2021, 31, 2010853. doi:10.1002/adfm.202010853
  • Hofmann, O. T.; Zojer, E.; Hörmann, L.; Jeindl, A.; Maurer, R. J. First-principles calculations of hybrid inorganic–organic interfaces: from state-of-the-art to best practice. Physical chemistry chemical physics : PCCP 2021, 23, 8132–8180. doi:10.1039/d0cp06605b
  • Fang, L.; Makkonen, E.; Todorović, M.; Rinke, P.; Chen, X. Efficient Amino Acid Conformer Search with Bayesian Optimization. Journal of chemical theory and computation 2021, 17, 1955–1966. doi:10.1021/acs.jctc.0c00648
Other Beilstein-Institut Open Science Activities