Project description
Machine learning spurs design of molecules with tailored magnetic and vibrational properties
Spin-phonon coupling, the coupling between electronic spins and lattice vibrations, results in spin relaxation. Spin relaxation is a particularly undesirable event in devices that rely on spin for information storage and quantum computing. Most efforts to date have been able to address this problem by maintaining extremely low temperatures. However, this will clearly not be practical for tomorrow's devices. Supporting innovation in spin-phonon coupling and magnetic molecules, the EU-funded AI-DEMON project will develop a computational framework exploiting machine learning and first-principles spin dynamics. Enhanced understanding of the mechanism of spin-phonon relaxation will enable the design of molecular prototypes with tailored magnetic and vibrational properties.
Objective
As technologies based on semiconductors and ferromagnets are reaching their limits in computational and memory-storage capabilities, new technologies based on spin are emerging as alternative. Magnetic molecules represent the ultimate small-scale magnetic unit that can be synthesized and processed into a device for spintronics and quantum computing applications but their use is confined to very low temperatures. The grand challenge of this proposal is to design magnetic molecules with long spin lifetime at ambient temperature by tuning the main microscopic interaction responsible for spin relaxation: the spin-phonon coupling. AI-DEMON will address this challenge by developing a novel first-principles and machine-learning computational framework able to cover all the essential aspects of the design of new coordination compounds with tailored properties. AI-DEMON has three main objectives, each one representing a major contribution to the field: i) I will unveil the mechanism of spin-phonon relaxation in magnetic molecules by developing a quantitative first-principles spin relaxation theory, ii) I will efficiently explore the chemical space of magnetic coordination compounds by developing a universal machine-learning model able to predict vibrational and magnetic properties, and iii) I will design molecular prototypes with tailored magnetic and vibrational properties by developing generative machine-learning methods. Preliminary results on spin relaxation theory and machine-learning applied to magnetic properties show great promise and set the cornerstone of the project. The use of novel methodologies, such as machine learning and first-principles spin dynamics, represent a strong disruption in the current approach to theoretical modelling and discovery of new magnetic molecules and will propel the field into a new and modern era. Significant impact beyond the field of molecular magnetism, e.g. bio-inorganic chemistry and solid-state qubits, can also be anticipated.
Fields of science
- natural sciencesphysical scienceselectromagnetism and electronicsspintronicsmolecular spintronics
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcomputer hardwarequantum computers
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
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Funding Scheme
ERC-STG - Starting GrantHost institution
D02 CX56 DUBLIN 2
Ireland