Precise Large-Scale Chemical Transformations on Surfaces: Deep Learning Meets Scanning Probe Microscopy with Interpretability
Autooss Video of Autooss monitoring the STM Image dataset for four image classification models Dataset for Model_target Dataset for Model_ind Dataset for Model_diss Dataset for Model_triple optimized neural network parameters (.pth) Theoretical calculations (DFT and BOSS) Reaction trajectories of three reactions (first Br removal, second Br removal and two Br removal concurrently) Density of states for ZnBr2Me4DPP Orbitals Charge density difference (ZnBr2Me4DPP, ZnBrMe4DPP and Me4DPP on Au(111) BOSS for phenyl group andm methyl group BOSS and DFT for configurations of ZnBr2Me4DPP and STM simulations Experimental characterization 1H-NMR spectrum of H2Br2Me4DPP in CDCl3 13C-NMR spectrum of H2Br2Me4DPP in CDCl3 MALDI-TOF mass spectrum of H2Br2Me4DPP. UV/vis spectrum of H2Br2Me4DPP in CHCl3 (conc. = 10 µM) 1H-NMR spectrum of ZnBr2Me4DPP in CDCl3 13C-NMR spectrum of ZnBr2Me4DPP in CDCl3 MALDI-TOF mass spectrum of ZnBr2Me4DPP UV/vis spectrum of ZnBr2Me4DPP in CHCl3 (conc. = 10 µM)
Institutions
- Universidad Autónoma de Madrid (ES)
- Swiss Federal Laboratories for Materials Science and Technology (CH)
- Aalto University (FI)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2024-09-14
- DOI
- https://doi.org/10.5281/zenodo.13761822
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00