The quest to design the new materials required to meet the challenge of net zero and a sustainable future may have taken a new direction thanks to recent research from the University of Liverpool.
The researchers from Liverpool demonstrated in their paper “Optimality Guarantees for Crystal Structure Prediction,” which was published in the scientific journal Nature, that a mathematical algorithm can guarantee to predict the structure of any material solely on the basis of knowledge of its atoms.
The algorithm, developed by researchers from the Departments of Chemistry and Computer Science at the University of Liverpool, systematically evaluates all possible sets of structures simultaneously rather than considering them one at a time to speed up finding the right solution.
“We were successful in developing a general algorithm for crystal structure prediction that can be applied to a wide range of structures. Combining local minimization with integer programming allows us to use strong optimization methods in a discrete space to explore unknown atomic places in the continuous space.”
Professor Paul Spirakis, from the University’s Department of Computer Science,
This leading edge makes it conceivable to recognize those materials that can be made and, generally speaking, to foresee their properties. Quantum computers, which can solve many problems faster than conventional computers and, as a result, accelerate the calculations even further, were used to demonstrate the new method.
Materials are essential to our way of life; “everything is made of something.” To meet the challenge of net zero, new materials are required, including catalysts for the production of clean polymers and chemicals for our sustainable future, batteries and solar absorbers for clean power, low-energy computing, and more.
Because there are so many possible combinations of atoms to form materials and structures, this research takes a long time and is challenging. Additionally, materials with transformative properties are likely to have structures that are distinct from those that are currently known, making it extremely difficult for scientists to predict a structure about which nothing is known.
“Having certainty in the prediction of crystal structures now offers the opportunity to identify precisely 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,” stated Professor Matt Rosseinsky of the University’s Department of Chemistry and Materials Innovation Factory.
“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 with this new tool.” “We will also be able to find materials that outperform those we rely on today, meeting the future challenges of a sustainable society.” “We will be able to find materials that outperform those we rely on today.”
“We managed to provide a general algorithm for crystal structure prediction that can be applied to a variety of structures,” stated Department of Computer Science Professor Paul Spirakis. We were able to use robust optimization techniques in discrete space to investigate the unknown atomic positions in continuous space by combining local minimization with integer programming.
“In the enjoyable adventure of discovering new materials that are useful, our goal is to explore and use more algorithmic ideas. Joining the endeavors of physicists and PC researchers was the way to this achievement.”
The Leverhulme Research Center for Functional Materials Design, which was established to develop new approaches to the design of functional materials at the atomic scale through interdisciplinary research, is a member of the research team. Other members of the team include researchers from the Materials Innovation Factory, the Departments of Computer Science and Chemistry, and the University of Liverpool.
More information: Matthew Rosseinsky, Optimality Guarantees for Crystal Structure Prediction, Nature (2023). DOI: 10.1038/s41586-023-06071-y. www.nature.com/articles/s41586-023-06071-y
