Summary
Functional materials with outstanding technological properties can be found under extreme pressures and temperatures. This is particularly true for nitrides and hydrides, where the application of high-pressure high-temperature (HPHT) conditions has recently revealed an unexpectedly rich and complex chemistry and enabled the synthesis of compounds showing outstanding mechanical and electronic properties with applications in electronics, hard coatings, hydrogen storage, superconductivity and many more. However, great challenges remain to be conquered in order to truly explore the possibilities permitted by these exotic materials. Indeed, their properties often vanish when brought back to ambient conditions, either because the atomic arrangement or the underlying physical processes becomes energetically unfavourable. Moreover, the importance of finite-T effects and the structural and dynamical complexity of these HPHT phases, prohibit computations so far from efficiently guiding experimental synthesis.
The goal of this project is to provide the computational tools for guiding the efficient and targeted synthesis of next-generation technological materials, including the choice of synthesis conditions and precursor materials. We will search for materials retaining their functional properties under decompression or are directly synthesizable at ambient pressure. To accomplish this, we will develop a work flow based on machine learning inter-atomic potentials to numerically explore experimental synthesis conditions at ab-initio accuracy. This will enable an analysis of thermodynamic competition between different phases at HPHT and rigorous benchmarking against experiments to ensure that we truly portray nature's behaviour.This project will open up uncharted horizons for exploiting pressure and temperature as thermodynamic variables to explore new chemistry and synthesis pathways, ultimately guiding experiments towards industrially relevant novel technological materials.
The goal of this project is to provide the computational tools for guiding the efficient and targeted synthesis of next-generation technological materials, including the choice of synthesis conditions and precursor materials. We will search for materials retaining their functional properties under decompression or are directly synthesizable at ambient pressure. To accomplish this, we will develop a work flow based on machine learning inter-atomic potentials to numerically explore experimental synthesis conditions at ab-initio accuracy. This will enable an analysis of thermodynamic competition between different phases at HPHT and rigorous benchmarking against experiments to ensure that we truly portray nature's behaviour.This project will open up uncharted horizons for exploiting pressure and temperature as thermodynamic variables to explore new chemistry and synthesis pathways, ultimately guiding experiments towards industrially relevant novel technological materials.
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| Web resources: | https://cordis.europa.eu/project/id/101117758 |
| Start date: | 01-03-2024 |
| End date: | 28-02-2029 |
| Total budget - Public funding: | 1 500 000,00 Euro - 1 500 000,00 Euro |
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Original description
Functional materials with outstanding technological properties can be found under extreme pressures and temperatures. This is particularly true for nitrides and hydrides, where the application of high-pressure high-temperature (HPHT) conditions has recently revealed an unexpectedly rich and complex chemistry and enabled the synthesis of compounds showing outstanding mechanical and electronic properties with applications in electronics, hard coatings, hydrogen storage, superconductivity and many more. However, great challenges remain to be conquered in order to truly explore the possibilities permitted by these exotic materials. Indeed, their properties often vanish when brought back to ambient conditions, either because the atomic arrangement or the underlying physical processes becomes energetically unfavourable. Moreover, the importance of finite-T effects and the structural and dynamical complexity of these HPHT phases, prohibit computations so far from efficiently guiding experimental synthesis.The goal of this project is to provide the computational tools for guiding the efficient and targeted synthesis of next-generation technological materials, including the choice of synthesis conditions and precursor materials. We will search for materials retaining their functional properties under decompression or are directly synthesizable at ambient pressure. To accomplish this, we will develop a work flow based on machine learning inter-atomic potentials to numerically explore experimental synthesis conditions at ab-initio accuracy. This will enable an analysis of thermodynamic competition between different phases at HPHT and rigorous benchmarking against experiments to ensure that we truly portray nature's behaviour.This project will open up uncharted horizons for exploiting pressure and temperature as thermodynamic variables to explore new chemistry and synthesis pathways, ultimately guiding experiments towards industrially relevant novel technological materials.
Status
SIGNEDCall topic
ERC-2023-STGUpdate Date
12-03-2024
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