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Artificial-Intelligence-driven Variable Assembly of Molecules

Artificial-Intelligence-driven Variable Assembly of Molecules

Oliver Hofmann (ORCID: 0000-0002-2120-3259)
  • Grant DOI 10.55776/FG3400
  • Funding program Research Groups
  • Status ongoing
  • Start June 1, 2025
  • End May 31, 2030
  • Funding amount € 1,190,703

Disciplines

Computer Sciences (30%); Mathematics (10%); Physics, Astronomy (60%)

Keywords

    Scanning Tunneling Micropscopy, Quantum Corrals, Machine Learning, Scattering Theory, Surface Science, Structure Search

Abstract

The project, "Artificial-Intelligence-driven Variable Assembly of Molecules on Surfaces" (AI-VAMOS), is a groundbreaking effort to revolutionize how we design and build tiny structures at the atomic level. Imagine creating new materials, computing devices, or even molecules, atom by atom, with incredible precision. AI-VAMOS aims to make this possible using cutting-edge artificial intelligence (AI) and advanced microscopy. The team wants to automate the creation of highly intricate structures using a Scanning Tunneling Microscope (STM). This device can move and position individual atoms or molecules on a surface. By combining STM technology with AI, the goal is to build structures that could lead to faster, energy-efficient computing and innovative materials that cant be made using traditional methods. The project uses AI to "teach" the STM to move molecules on a surface autonomously. By integrating advanced machine learning techniques like deep reinforcement learning, the system learns from experiments, adapting its strategies to achieve the best results. The AI will control the microscope to place molecules with atomic precision, overcoming challenges like unpredictable molecule movements and complex interactions on surfaces. This technology could lead to: 1. Revolutionary Computing: Creating quantum corralsstructures that can manipulate electrons to perform logic operations, a novel approach for future computing technologies. 2. New Materials: Designing materials with unique properties, from better semiconductors to stronger, light weight composites. 3. Scientific Insights: Advancing our understanding of how molecules interact at the atomic level, opening doors to discoveries in physics, chemistry, and materials science. A team of experts from diverse fieldsphysics, chemistry, mathematics, and AIis driving this project. Theyll combine their skills to ensure the technology is safe and efficient. The impact of this project extends far beyond science labs. By making it easier to work at the atomic level, it could spark innovations in energy, medicine, and electronics. Its a step toward realizing visionary physicist Richard Feynmans dream of a world where we manipulate individual atoms to create anything imaginable.

Consortium
  • Bettina Könighofer, Technische Universität Graz
    consortium member (01.06.2025 -)
  • Jussi Behrndt, Technische Universität Graz
    consortium member (01.06.2025 -)
  • Leonhard Grill, Universität Graz
    consortium member (01.06.2025 -)
  • Markus Aichhorn, Technische Universität Graz
    consortium member (01.06.2025 -)
  • Oliver Hofmann, Technische Universität Graz
    consortium member (01.06.2025 -)
  • Oliver Hofmann, Technische Universität Graz
    coordinator (01.06.2025 -)
Research institution(s)
  • Technische Universität Graz
International project participants
  • Vladimir Lotoreichik, Czech Academy of Sciences - Czechia
  • Nils Jansen, Ruhr-Universität Bochum - Germany
  • Jernej Mravlje, Jozef Stefan Institute - Slovenia

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