Precision nanofabrication techniques are essential for assembling nanomaterials with functionalities that promise to revolutionize electronics, medical advancements and optics. The scanning probe microscopy (SPM) techniques have emerged as a promising approach to fabricate nanostructures. However, the selection of proper parameters is time-consuming and requires extensive domain knowledge. Therefore, new efficient and autonomous SPM techniques are needed to reduce the reliance on user supervision and learn optimal strategies for new systems. In this proposal, I will develop deep learning approaches to automate synthesizing a whole Ullmann reaction with the precursor Zn(II)-5,15-bis(4-bromo-2,6-dimethylphenyl)porphyrin (ZnBr2Me4DPP)) on TMDs which includes the dissociation of C-Br bonds and association of C-C bonds and movement of fragments, and the assembly of large nanostructures in STM by combining DFT calculations and machine learning interatomic potentials.

2025

2029