SOURCE-LINKED INTELLIGENCE

Understanding Material Synthesis Conditions and Complexity at High-Pressure

CORDIS · observation · Publication date unknown

als. 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. Materials Modeling From First Principles, Nitrides, Hydrides, Extreme Conditions, Machine Learning Inter-Atomic Potentials, Density Functional Theory, Computational Material Exploration

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recordType

award

status

SIGNED

region

EU

value

1500000

unit

EUR