QC Mineral – Quantum computing for mineral materials

Project duration: 1.03.2024 – 31.12.2024

ApplicationsMaterials science

Optimisation of process-relevant material characteristics for industrial applications.

We develop quantum computing-based atomistic simulation approaches for the investigation of compositional and doping effects in structurally complex crystalline and amorphous oxide materials with the aim of optimising process-relevant material characteristics for industrial applications.

The aim of the QCMineral project is to develop mineral materials and processes for use in the production of renewable, carbon-neutral energy and fuels, as well as the optimisation and development of new types of glass and glass-ceramics. A particular challenge for computer-aided material development lies in the description of complex chemical compositions and the mapping of the irregular atomic structure of glass-like materials. While this project overtaxes even the most powerful classical hardware, the targeted use of quantum computers promises a substantial gain in simulatable material systems and a drastic reduction in the development time of new materials.

Motivation

The QCMineral project aims to develop mineral materials and processes for use in the production of renewable, carbon-neutral energy and fuels as well as the development of new types of amorphous functional materials. From a materials science perspective, this includes the optimisation and new development of perovskite-like metal oxides and (partially) amorphous oxide solids such as glasses and glass ceramics. In both applications, the addition of tiny amounts of additional elements (doping) plays a fundamental role in the targeted adjustment of functionally relevant material characteristics.

The use of atomistic simulations to accelerate the identification of suitable redox materials for solar-powered thermochemical energy generation processes, as well as the targeted optimisation of glass ceramics for everyday and high-tech applications, represents a key to the competitiveness of the domestic economy.

Challenge

The performance of conventional simulation methods used in industrial and academic research is essentially based on the utilisation of long-range order properties (i.e. symmetries). On the other hand, modelling the complex atomic structure and chemical composition of real materials, which determine the material properties, requires the use of large (>> 10³ atoms) simulation cells, whose exact calculability on purely classical hardware is severely limited. The project therefore aims to create a hybrid quantum-classical materials development platform by combining existing classical materials simulation environments from industry with powerful quantum computing-based simulation approaches developed by DLR, which, supplemented by machine learning methods, is capable of efficiently solving real materials science problems in industrial research.

All news