Descripción del proyecto
El aprendizaje automático impulsa el diseño de moléculas con propiedades magnéticas y vibratorias a medida
El acoplamiento espín-fonón, es decir, el acoplamiento entre los espines electrónicos y las vibraciones de la red, da lugar a la relajación del espín. La relajación del espín es un fenómeno particularmente indeseable en los dispositivos que dependen del espín para el almacenamiento de información y la computación cuántica. La mayoría de los esfuerzos realizados hasta la fecha han podido resolver este problema manteniendo temperaturas extremadamente bajas. Sin embargo, está claro que esto no será práctico para los dispositivos del futuro. El equipo del proyecto AI-DEMON, financiado con fondos europeos, apoyará la innovación en el acoplamiento espín-fonón y en las moléculas magnéticas, y desarrollará un marco computacional que aprovechará el aprendizaje automático y la dinámica de espín de primeros principios. Una mejor comprensión del mecanismo de relajación por acoplamiento espín-fonón permitirá el diseño de prototipos moleculares con propiedades magnéticas y vibratorias a medida.
Objetivo
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.
Ámbito científico
- natural sciencesphysical scienceselectromagnetism and electronicsspintronicsmolecular spintronics
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcomputer hardwarequantum computers
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Palabras clave
Programa(s)
Régimen de financiación
ERC-STG - Starting GrantInstitución de acogida
D02 CX56 Dublin
Irlanda