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sustainable technologies 
 A mathematical algorithm developed by University of Liverpool researchers
could signal a step change in the quest to design the new materials that are
needed to meet the challenge of net zero and a sustainable future. 

  Date:
      July 5, 2023
  Source:
      University of Liverpool
  Summary:
      New research could signal a step change in the quest to design the
      new materials that are needed to meet the challenge of net zero and
      a sustainable future. Researchers have shown that a mathematical
      algorithm can guarantee to predict the structure of any material
      just based on knowledge of the atoms that make it up.


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FULL STORY
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New research by the University of Liverpool could signal a step change
in the quest to design the new materials that are needed to meet the
challenge of net zero and a sustainable future.

Publishing in the journal Nature, the Liverpool researchers have shown
that a mathematical algorithm can guarantee to predict the structure of
any material just based on knowledge of the atoms that make it up.

Developed by an interdisciplinary team of researchers from the University
of Liverpool's Departments of Chemistry and Computer Science, the
algorithm systematically evaluates entire sets of possible structures
at once, rather than considering them one at a time, to accelerate
identification of the correct solution.

This breakthrough makes it possible to identify those materials that can
be made and, in many cases, to predict their properties. The new method
was demonstrated on quantum computers that have the potential to solve
many problems faster than classical computers and can therefore speed
up the calculations even further.

Our way of life depends on materials -- "everything is made of
something." New materials are needed to meet the challenge of net
zero, from batteries and solar absorbers for clean power to providing
low-energy computing and the catalysts that will make the clean polymers
and chemicals for our sustainable future.

This search is slow and difficult because there are so many ways that
atoms could be combined to make materials, and in particular so many
structures that could form. In addition, materials with transformative
properties are likely to have structures that are different from those
that are known today, and predicting a structure that nothing is known
about is a tremendous scientific challenge.

Professor Matt Rosseinsky, from the University's Department of Chemistry
and Materials Innovation Factory, said: "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
synthesised and the structures that they will adopt, giving us for the
first time the ability to define the platform for future technologies.

"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."  Professor Paul Spirakis, from the
University's Department of Computer Science, said: "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."  The research team
includes researchers from the University of Liverpool's Departments of
Computer Science and Chemistry, the Materials Innovation Factory and
the Leverhulme Research Centre for Functional Materials Design, which
was established to develop new approaches to the design of functional
materials at the atomic scale through interdisciplinary research.

This project has received funding from the Leverhulme Trust and the
Royal Society.

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==========================================================================
Journal Reference:
   1. Vladimir V. Gusev, Duncan Adamson, Argyrios Deligkas, Dmytro
   Antypov,
      Christopher M. Collins, Piotr Krysta, Igor Potapov,
      George R. Darling, Matthew S. Dyer, Paul Spirakis,
      Matthew J. Rosseinsky. Optimality guarantees for crystal
      structure prediction. Nature, 2023; 619 (7968): 68 DOI:
      10.1038/s41586-023-06071-y
==========================================================================

Link to news story:
https://www.sciencedaily.com/releases/2023/07/230705115134.htm

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