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Intelligent synthesis of magnetic nanographenes via chemist-intuited atomic robotic probe

Nature Synthesis volume 3, pages 466–476 (2024)Cite this article

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Abstract

Atomic-scale manufacturing of carbon-based quantum materials with single-bond precision holds immense potential in advancing tailor-made quantum materials with unconventional properties, which are crucial in developing next-generation spintronic devices and quantum information technologies. On-surface chemistry approaches, including surface-assisted synthesis and probe-assisted manipulation, are impeded by challenges in reaction selectivity control or restricted by scalability and production efficiency. Here we demonstrate the concept of the chemist-intuited atomic robotic probe by integrating probe chemistry knowledge and artificial intelligence, allowing for atomically precise single-molecule manipulation to fabricate single-molecule quantum π-magnets with single-bond precision. Our deep neural networks not only transform complex probe chemistry into machine-understandable tasks but also provide chemist intuition to elusive reaction mechanisms by extracting the critical chemical information within the data. A joint experimental and theoretical investigation demonstrates that a voltage-controlled two-electron-assisted electronic excitation enables synchronous six-bond transformations to extend the zigzag edge topology of single-molecule quantum π-magnets, triggered by phenyl C(sp2)–H bond activation, which aligns with initial conjectures given by the deep neural models. Our work represents a transition from autonomous fabrication to intelligent synthesis with levels of selectivity and precision beyond current synthetic tools for improved synthesis of organic quantum materials towards on-chip integration.

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Fig. 1: The concept of the CARP for precise site-selective single-molecule manipulation of quantum π-magnets.
Fig. 2: Structural and electronic characterizations of a series of zigzag-edged SMQMs created via the site-selective probe chemistry reactions.
Fig. 3: The infrastructure of the STM-AI system within the CARP framework.
Fig. 4: Interpreting the information captured by the STM-Analyst to understand the mechanism of synthesizing SMQMs by probe chemistry.
Fig. 5: Computational simulations of multiple-bond cyclodehydrogenation process for site-selective synthesis of SMQMs.

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Data availability

All experimental and theoretical calculation data are available in the main text or Supplementary Information.

Code availability

Codes for deep neural networks and STM-AI platform are available at GitHub (https://github.com/jiali1025/Intelligent-topological-engineering-of-quantum-p-magnets).

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Acknowledgements

J.L. acknowledges the support from the NRF, Prime Minister’s Office, Singapore, under the Competitive Research Program Award (NRF-CRP29-2022-0004), MOE Tier 2 grants (MOE-T2EP10221-0005 and MOE-T2EP10123-0004), Agency for Science, Technology and Research (A*STAR) under MTC Individual Research Grants (Project ID M21K2c0113) and Science and Technology Project of Jiangsu Province (grant number BZ2022056). X.W. acknowledges the support from the National Key R&D Program of China (no. 2022ZD0117501), Agency for Science, Technology and Research (A*STAR) Singapore RIE2020 Advanced Manufacturing and Engineering Programmatic Grant A1898b0043, and Tsinghua University Initiative Scientific Research Program. J.S. acknowledges the support from Agency for Science, Technology and Research (A*STAR) Advanced Manufacturing & Engineering (AME) Young Individual Research Grant (YIRG) A2084c0171. C.Z. acknowledges the support from MOE Tier 2 grants (MOE2019-T2-2-030), and computational resources in NUS High Performance Computing (HPC) facilities and National Supercomputing Centre (NSCC) in Singapore.

Author information

Author notes

  1. These authors contributed equally: Jie Su, Jiali Li, Na Guo.

Authors and Affiliations

  1. Department of Chemistry, National University of Singapore, Singapore, Singapore

    Jie Su, Xinnan Peng, Pin Lyu, Jishan Wu, Chun Zhang & Jiong Lu

  2. Department of Chemical and Biomolecular Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore

    Jiali Li, Jun Yin & Zhiyao Luo

  3. National University of Singapore Chongqing Research Institute, Chongqing, China

    Na Guo & Chun Zhang

  4. Department of Physics, National University of Singapore, Singapore, Singapore

    Na Guo & Chun Zhang

  5. Department of Electrical and Computer Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore

    Jiahao Wang & Koen Mouthaan

  6. Department of Chemical Engineering, Tsinghua University, Beijing, China

    Xiaonan Wang

  7. Institute for Functional Intelligent Materials, National University of Singapore, Singapore, Singapore

    Jiong Lu

  8. National University of Singapore (Suzhou) Research Institute, Suzhou, China

    Jiong Lu

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Contributions

C.Z., X.W. and J. Lu supervised the project. J.S., J. Li, X.W. and J. Lu conceptualized this project. J. Wu synthesized and characterized molecular precursor. J.S. and X.P. performed the on-surface experiments and analysis. N.G. and C.Z. performed the DFT calculations. P.L. performed the illustration of conceptual figure. J. Li, J.Y., J. Wang, Z.L., J.S., K.M., J. Lu and X.W. designed and constructed the deep neural networks. All authors contributed towards writing the manuscript.

Corresponding authors

Correspondence to Chun Zhang, Xiaonan Wang or Jiong Lu.

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Nature Synthesis thanks Shiyong Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Peter Seavill, in collaboration with the Nature Synthesis team.

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Extended data

Extended Data Fig. 1 Additional nc-AFM characterization data for molecule 1 and 2.

Experimental (a) and simulated AFM (b) image of a single molecule 1 on Au(111) surface. Scanning parameters of (a): Setpoint before switching off the feedback: Vs = 0.5 V, It = 400 pA. Scale bar: 4 Å. (c-d) Force spectroscopic measurements taken at different sites over a single molecule 2. The height difference between the cyclized area (lower panel c) and non-cyclized area (upper panel c) is ~150 pm, indicating that the non-cyclized benzylic groups remain intact after cyclizing the adjacent corner. Numbers indicate the positions where the spectra are recorded. Scanning parameters: Vs = 1 mV. Setpoint before switching off the feedback: Vs = 0.5 V, It = 400 pA. Scale bar: 4 Å.

Source data

Extended Data Fig. 2 Electronic structure of the molecular products obtained via sequential probe-assisted cyclodehydrogenation.

(a) Spin-polarized DFT calculations of molecular orbital diagrams of freestanding molecule 1 and molecule 3. (b) dI/dV spectra acquired at the edge of molecule 1 and 3 with the reference spectra collected over the bare Au(111) surface. dI/dV spectrum collected at the zigzag edge of molecule 1 shows two pronounced peaks located at −1.25 V (±0.05 V) and +1.46 V (±0.05 V), respectively (Upper curve). Similarly, dI/dV spectrum collected at the edges of molecule 3 after a second cyclization shows two small peaks (Middle curve) located at −0.54 V (±0.05 V) and +1.12 V (±0.05 V). As illustrated in Extended Data Fig. 2a, the frontier orbitals of a neutral molecule 1 contain two degenerate singly occupied molecular orbitals (ψ5↑ and ψ6↑, denoted as SOMOs) at −0.52 eV below EF and two singly unoccupied molecular orbitals (ψ5↓ and ψ6↓), denoted as SUMOs) at +1.47 eV above EF, in line with the experimental energetic position of occupied (−1.25 V) and unoccupied (+1.46 V) electronic states. Similarly, a neutral molecule 3 is predicted to have two pairs of frontier orbitals assigned to the SOMOs (ψ5↑ and ψ6↑, −0.47 eV) and SUMOs (ψ5↓ and ψ6↓, +1.11 eV), consistent with the experimentally recorded electronic states at −0.54 V and +1.12 V, respectively (Extended Data Fig. 2a, right). (c) Point dI/dV spectra acquired over different sites (indicated by coloured circles in the STM images shown on the right) of molecule 1–4 on Au(111) surface. Scale bar: 5 Å.

Source data

Extended Data Fig. 3 Additional dI/dV imaging and spectra measurements of molecule 1 and molecule 3.

Constant-current dI/dV maps of the SOMOs and SUMOs of molecule 1 (a-b) and the corresponding STM images (d-e) taken simultaneously. Constant-current dI/dV maps of the SOMOs and SUMOs of molecule 3 (c-d) and the corresponding STM images (f-g) taken simultaneously. (h) dI/dV spectra recorded near the EF. The curve corresponds to the red circle shown in (f). Setpoint: Vs = 0.06 V, It = 1.2 nA. Modulation voltage: 2 mV. Scale bar: 5 Å.

Source data

Extended Data Fig. 4 DFT-calculated cyclization of molecule 2 to form molecule 3, and the cyclization of molecule 3 to form molecule 4.

DFT-calculated molecular structures of the cyclization process from molecule 2 to 3 (a–e) and 3 to 4 (g–k). (f) The DFT-calculated energy diagram for the cyclization process of molecule 2 to 3 (f), and molecule 3 to 4 (i).

Extended Data Fig. 5 Schematic representation of the workflow and underlying mechanisms for interpreting the trained model.

(a) Application of a game-theoretic approach to decode the trained models. (b) Construction of feature blocks from STM images employed in model explanation analysis. Details can be referred to Supplementary Information Section 5.

Supplementary information

Supplementary Information

Experimental details and Supplementary Notes Sections 1–5, Figs. 1–36 and Tables 1 and 2.

Supplementary Video 1

Video demonstration of the CARP operation.

Source data

Source Data Fig. 5

Statistical source data.

Source Data Extended Data Fig./Table 1

Statistical source data.

Source Data Extended Data Fig./Table 2

Statistical source data.

Source Data Extended Data Fig./Table 3

Statistical source data.

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Su, J., Li, J., Guo, N. et al. Intelligent synthesis of magnetic nanographenes via chemist-intuited atomic robotic probe. Nat. Synth 3, 466–476 (2024). https://doi.org/10.1038/s44160-024-00488-7

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