MODA for modelling the inverse piezo-effect in piezo-electric semiconductors

The AddMorePower consortium is dedicated to developing characterization and modelling methods that can meet the unique needs of upcoming power semiconductor technology generations. To achieve effective and efficient research and innovation, a seamless interoperation between materials characterization, materials modelling, and data science is required. This goal can only be accomplished through the use of FAIR and open data practices. AddMorePower employs the CHaracterization DAta (CHADA) and MOdelling DAta (MODA) methodologies to achieve FAIR data management. These systematic description and documentation methods are used for materials characterization data and materials modelling data, respectively. They are based on a common terminology, concepts and relationships defined by the community, providing a holistic approach to combine data from various materials characterization and modelling techniques, which allows analysing and modelling of complex structures. In this document the MODA for modelling the piezo effect in gallium nitride (GaN) transistors simulated in the project AddMorePower is presented. The MODA is based on the template released by emmc.eu in May 2021 and the common terminology defined in the CEN Workshop Agreement 17284.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-02
DOI
https://doi.org/10.5281/zenodo.21128138
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

MODA for modelling the inverse piezo-effect in piezo-electric semiconductors

Manuel Petersmann
Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
article

MODA for modelling the inverse piezo-effect in piezo-electric semiconductors

Manuel Petersmann
article en

Abstract

The AddMorePower consortium is dedicated to developing characterization and modelling methods that can meet the unique needs of upcoming power semiconductor technology generations. To achieve effective and efficient research and innovation, a seamless interoperation between materials characterization, materials modelling, and data science is required. This goal can only be accomplished through the use of FAIR and open data practices. AddMorePower employs the CHaracterization DAta (CHADA) and MOdelling DAta (MODA) methodologies to achieve FAIR data management. These systematic description and documentation methods are used for materials characterization data and materials modelling data, respectively. They are based on a common terminology, concepts and relationships defined by the community, providing a holistic approach to combine data from various materials characterization and modelling techniques, which allows analysing and modelling of complex structures. In this document the MODA for modelling the piezo effect in gallium nitride (GaN) transistors simulated in the project AddMorePower is presented. The MODA is based on the template released by emmc.eu in May 2021 and the common terminology defined in the CEN Workshop Agreement 17284.

Zenodo (CERN European Organization for Nuclear Research)
empirica - Communication and Technology Research (DE)
European Commission
Industry, innovation and infrastructure
Openalex Percentile: Top 17%
Machine Learning in Materials Science
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MODA for modelling the inverse piezo-effect in piezo-electric semiconductors — Manuel Petersmann · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS