A recent study by the University of Liverpool could mark a significant milestone in the quest to create new materials that are able to meet the challenge of net zero and a sustainable future.
The atomistic structure of the crystalline material garnet corresponds to the crater on the potential energy surface full of rough mountains, hills, and valleys. Finding it computationally is very hard, but by fixing a mesh on this surface, advanced algorithms and quantum computers can be used to find the lowest-lying vertex. A subsequent tweak reveals the garnet structure, which comes with the optimality guarantee. Image Credit: University of Liverpool.
Researchers at Liverpool have demonstrated in the journal Nature that a mathematical algorithm can predict the structure of any material just based on knowledge of the atoms that make it up.
The algorithm, created by an interdisciplinary group of researchers from the Departments of Chemistry and Computer Science at the University of Liverpool, methodically assesses entire sets of possible structures at once, instead of considering them one at a time, to speed up the identification of the correct solution.
This achievement now means that the materials that can be made can be identified and, in many cases, their properties predicted. The novel method was illustrated on quantum computers, which have the potential to solve numerous problems faster than classical computers, allowing the calculations to be even faster.
Materials determine the way of life —“everything is made of something.” To meet the challenge of net zero, new materials are a must, ranging from batteries and solar absorbers for clean power to low-energy computing and catalysts for the production of clean polymers and chemicals for our sustainable future.
This search is slow and challenging as there are so many different ways in which atoms can be combined to make materials and, in particular, so many structures that could form as a result.
In addition, materials with transformative properties are likely to have different structures from those that are known today, and predicting a structure that nothing is known about is a tremendous scientific challenge.
Having certainty in the prediction of crystal structures now offers the opportunity to identify from the whole of the space of chemistry exactly which materials can be synthesized and the structures that they will adopt, giving us for the first time the ability to define the platform for future technologies.
Matt Rosseinsky, Professor, Department of Chemistry and Materials Innovation Factory, University of Liverpool
Matt Rosseinsky adds, “With this new tool, we will be able to define how to use those chemical elements that are widely available and begin to create materials to replace those based on scarce or toxic elements, as well as to find materials that outperform those we rely on today, meeting the future challenges of a sustainable society.”
We managed to provide a general algorithm for crystal structure prediction that can be applied to a diversity of structures. Coupling local minimization to integer programming allowed us to explore the unknown atomic positions in the continuous space using strong optimization methods in a discrete space. Our aim is to explore and use more algorithmic ideas in the nice adventure of discovering new and useful materials. Joining efforts of chemists and computer scientists was the key to this success.
Paul Spirakis, Professor, Department of Computer Science, University of Liverpool
The research was published in the journal Nature.
The study team is composed of scientists from the Departments of Computer Science and Chemistry at the University of Liverpool, as well as the Materials Innovation Factory and the Leverhulme Research Centre for Functional Materials Design, which was formed to create new methods for the design of functional materials at the atomic scale through interdisciplinary research.
The Royal Society and the Leverhulme Trust provided funding for this project.
Journal Reference:
Gusev, V. V., et al. (2023). Optimality guarantees for crystal structure prediction. Nature. doi.org/10.1038/s41586-023-06071-y.
Source: https://www.liverpool.ac.uk