Understanding Material Synthesis Conditions and Complexity at High-Pressure

Project Information

UNMASCC-HP

Grant agreement ID: 101117758

DOI

10.3030/101117758

EC signature date 24 November 2023

Start date 1 March 2024

End date 28 February 2029

Funded under

• European Research Council (ERC)

Total cost

€ 1 500 000,00

EU contribution

€ 1 500 000,00

Coordinated by

LINKOPINGS UNIVERSITET
Sweden

Periodic Reporting for period 1 - UNMASCC-HP (Understanding Material Synthesis Conditions and Complexity at High-Pressure)

Reporting period: 2024-03-01 to 2026-08-31

Summary of the context and overall objectives of the project

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 workflow 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 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.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The first two years of the project focused on the development of advanced computational methodologies for materials discovery under extreme conditions and their application to nitride, carbo‑nitride, and hydride systems. During the first phase, substantial effort was devoted to creating scalable symmetry‑aware structure generation and screening methods, enabling the systematic exploration of previously inaccessible regions of chemico-structural space. A highly optimized workflow was established that allows the symmetry‑restricted exploration of crystal structures at unprecedented scale, allowing to investigate billions of candidate structures per day on modern GPU architectures.
In parallel, new scientific insights were obtained through close collaboration of theory with experimental synthesis. Several nitride and carbo‑nitride systems were investigated, revealing recoverable high‑pressure phases with complex electronic behaviour and even layered motifs.
To investigate hydrogen‑rich systems, a finite‑temperature finite-pressure structure relaxation workflow was developed, allowing anharmonic and thermal effects to be incorporated efficiently under symmetry constraints. This enables reliable structural refinement at finite-temperature —including quantum effects (no tunnelling)— at significantly lower computational cost than fully quantum approaches. In addition, a new benchmark methodology for machine‑learned interatomic potentials was established based on symmetry‑restricted energy landscapes, addressing a critical gap in evaluating these models for unknown crystal prototypes. The project also identified the need for advanced analysis tools, leading to the development of an open‑source, web‑based platform for interactive visualization and comparison of crystal structures.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The project has so far delivered advanced scalable methodologies for GPU‑native crystal structure generation, large‑scale screening, and finite‑temperature modelling under symmetry constraints beyond the state-of-the-art. We investigated the stability and properties of multiple novel and recoverable high‑pressure nitride and carbo‑nitride materials. The generated knowledge and open‑source software ecosystem support reproducible, high‑throughput materials discovery, as well as benchmarking, analysis, and visualization, with low barriers for adapting the methods across a wide range of topics in computational materials science. The tools enable efficient and tractable exploration of materials at extreme conditions through scalable, chemistry‑agnostic workflows applicable to complex systems, including hydrogen‑rich materials.
Large‑scale validation will be essential to assess robustness and transferability. The developed tools will be made available to the community, with an open invitation for further extension, refinement and collaboration. The remaining project will focus on continued methodological development and systematic application of the methods to a wide range of hydrogen and nitrogen containing compounds.