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aB IniTio calculations and MAchine learning for suPerconducting collective pheno...

aB IniTio calculations and MAchine learning for suPerconducting collective phenomena in novel materials "The aim of the BITMAP project ""aB-IniTio calculations and MAchine learning for suPerconducting collective phenomena in novel materials"" is to propose a workflow based on the combination of realistic Density Functional Theory (D... see more

31/01/2024

UNIBO

269K€

Project Budget: 269K€

Project leader

ALMA MATER STUDIORUM UNIVERSITA DI BOLOGNA No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

PARTICIPATION DEPralty Sin fecha límite de participación.

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Financing granted El organismo H2020 notifico la concesión del proyecto The day 2024-01-31

MSCA-IF-2019: Individual Fellowships Objective:The goal of the Individual Fellowships is to enhance the creative and innovative potential...

I+D

Cerrada does 5 years

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characteristics of the participant

Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

Private Information

No hay información privada compartida para este proyecto. Habla con el coordinador.

4 Participantes

UNIBO

269.00K€ | Leader

ACEITES ESPECIALES DEL MEDIT...

312.51K€ | participant

THE SIMONS FOUNDATION, INC

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Project duration: 45 months Date Start: 2020-04-10
End date: 2024-01-31

Project leader

ALMA MATER STUDIORUM UNIVERSITA DI BOLOGNA No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Project Budget 269K€

participation deadline Sin fecha límite de participación.

Project description "The aim of the BITMAP project ""aB-IniTio calculations and MAchine learning for suPerconducting collective phenomena in novel materials"" is to propose a workflow based on the combination of realistic Density Functional Theory (DFT) calculations with the Renormalization Group (RG) approach to superconducting Fermi surface instabilities. The latter is based on the pioneering work of Kohn-Luttinger where one can integrate out the high energy degrees of freedom perturbatively, and obtain effective attractive BCS interactions in non-s-wave channels. Once the superconducting pairing is known, as encoded in the superconducting gap function, a machine learning-based diagnostic procedure of the topological properties will be performed, upon the creation of specific ad-hoc convolutional neural networks. The project will allow the experience researcher to merge his present skills in the computational modeling of complex materials with modern concepts of machine learning, a sector that nowadays is expanding fast enough to easily foresee its applications in everyday life."

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