National University of Singapore (NUS) scientists have developed a new method for creating carbon-based quantum materials using artificial intelligence (AI) and scanning probe microscopy techniques. The team, led by Associate Professors Jiong Lu and Chun Zhang, introduced the concept of the chemist-intuited atomic robotic probe (CARP), which uses AI to fabricate and analyze magnetic nanographenes at the single-molecule level. This breakthrough could enhance control over atomic manufacturing, benefiting research and future applications, including developing high-speed electronic devices and quantum bits for quantum computers. The research was published in the journal Nature Synthesis.
AI-Driven Atomic Robotic Probe Enhances Quantum Material Manufacturing
Scientists from the National University of Singapore (NUS) have developed a novel method for fabricating carbon-based quantum materials at the atomic scale. This method combines scanning probe microscopy techniques with deep neural networks, demonstrating the potential of artificial intelligence (AI) in atomic manufacturing. This advancement could have significant implications for both fundamental research and future applications.
Open-shell magnetic nanographenes, a new class of carbon-based quantum materials, are of particular interest due to their robust π-spin centres and non-trivial collective quantum magnetism. These properties are vital for developing high-speed electronic devices at the molecular level and creating quantum bits, the fundamental units of quantum computers. Despite progress in synthesizing these materials through on-surface synthesis, precise fabrication and tailoring of these quantum materials at the atomic level have remained challenging.
The Chemist-Intuited Atomic Robotic Probe (CARP) Concept
The research team, led by Associate Professors Jiong Lu and Chun Zhang from NUS, introduced the concept of the chemist-intuited atomic robotic probe (CARP). This concept integrates probe chemistry knowledge and AI to fabricate and characterise open-shell magnetic nanographenes at the single-molecule level. This allows for precise engineering of their π-electron topology and spin configurations in an automated manner, mirroring the capabilities of human chemists.
The CARP concept uses deep neural networks trained with the experience and knowledge of surface science chemists to autonomously synthesize open-shell magnetic nanographenes. It can also extract chemical information from the experimental training database, offering conjectures about unknown mechanisms. This serves as an essential supplement to theoretical simulations, contributing to a more comprehensive understanding of probe chemistry reaction mechanisms.
Testing the CARP Concept
The researchers tested the CARP concept on a complex site-selective cyclodehydrogenation reaction used for producing chemical compounds with specific structural and electronic properties. The results showed that the CARP framework could efficiently adopt the expert knowledge of the scientist and convert it into machine-understandable tasks. This mimics the workflow to perform single-molecule reactions that can manipulate the geometric shape and spin characteristic of the final chemical compound.
Furthermore, the research team aims to harness the full potential of AI capabilities by extracting hidden insights from the database. They established a smart learning paradigm using a game theory-based approach to examine the framework’s learning outcomes. The analysis shows that CARP effectively captured important details that humans might miss, especially when it comes to making the cyclodehydrogenation reaction successful.
Future Goals and Applications of CARP
“Our main goal is to work at the atomic level to create, study and control these quantum materials. We are striving to revolutionise the production of these materials on surfaces to enable more control over their outcomes, right down to the level of individual atoms and bonds,” said Prof Lu.
The team plans to extend the CARP framework further to adopt versatile on-surface probe chemistry reactions with scale and efficiency. This could transform the conventional laboratory-based on-surface synthesis process towards on-chip fabrication for practical applications. Such a transformation could play a crucial role in accelerating the fundamental research of quantum materials and ushering in a new era of intelligent atomic fabrication.
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