Toma Susi
Papers
1
Total Citations
32
H-Index
1
About
Toma Susi is a leading figure in the atomic-scale characterization and manipulation of materials, with a particular focus on two-dimensional crystals and scanning transmission electron microscopy (STEM). His research bridges the gap between experimental imaging and computational modeling, enabling the precise identification and control of individual atoms within materials. Susi is perhaps best known for pioneering the use of deep learning and machine learning techniques to analyze electron microscopy data, a contribution that has revolutionized how researchers extract quantitative information from noisy, high-resolution images. His highly cited 2022 work, "Deep learning for electron and scanning probe microscopy: From materials design to atomic fabrication," has garnered 32 citations and serves as a foundational reference for the field. Beyond this, his work on single-atom dopants in graphene and the development of automated atomic fabrication methods has opened new avenues for quantum materials and nanoscale device engineering. With a growing citation record and a reputation for methodological innovation, Susi continues to shape the future of atomic-resolution microscopy and materials design.
Research Focus
Key Achievements
Top Papers
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