New AI tool accelerates discovery of truly new materials
The new artificial intelligence tool has already led to the discovery of
four new materials

Date:
September 21, 2021
Source:
University of Liverpool
Summary:
Researchers have created an artificial intelligence tool that
reduces the time and effort required to discover truly new
materials, and has already led to the discovery of four new
materials.



FULL STORY ========================================================================== Researchers at the University of Liverpool have created a collaborative artificial intelligence tool that reduces the time and effort required
to discover truly new materials.


========================================================================== Reported in the journal Nature Communications, the new tool has already
led to the discovery of fournew materials including a new family of solid
state materials that conduct lithium. Such solid electrolytes will be
key to the development of solid state batteries offering longer range
and increased safety for electric vehicles. Further promising materials
are in development.

The tool brings together artificial intelligence with human knowledge to prioritise those parts of unexplored chemical space where new functional materials are most likely to be found.

Discovering new functional materials is a high-risk, complex and often
long journey as there is an infinite space of possible materials
accessible by combining all of the elements in the periodic table,
and it is not known where new materials exist.

The new AI tool was developed by a team of researchers from the University
of Liverpool's Department of Chemistry and Materials Innovation Factory,
led by Professor Matt Rosseinsky, to address this challenge.

The tool examines the relationships between known materials at a scale unachievable by humans. These relationships are used to identify and numerically rank combinations of elements that are likely to form new materials. The rankings are used by scientists to guide exploration of
the large unknown chemical space in a targeted way, making experimental investigation far more efficient. Those scientists make the final
decisions, informed by the different perspective offered by the AI.

Lead author of the paper Professor Matt Rosseinsky said: "To date, a
common and powerful approach has been to design new materials by close
analogy with existing ones, but this often leads to materials that are
similar to ones we already have.

"We therefore need new tools that reduce the time and effort required to discover truly new materials, such as the one developed here that combines artificial intelligence and human intelligence to get the best of both.

"This collaborative approach combines the ability of computers to look
at the relationships between several hundred thousand known materials,
a scale unattainable for humans, and the expert knowledge and critical
thinking of human researchers that leads to creative advances.

"This tool is an example of one of many collaborative artificial
intelligence approaches likely to benefit scientists in the future."
Society's capacity to solve global challenges such as energy and
sustainability is constrained by our capability to design and make
materials with targeted functions, such as better solar absorbers making
better solar panels or superior battery materials making longer range
electric cars, or replacing existing materials by using less toxic or
scarce elements.

These new materials create societal benefit by driving new technologies to tackle global challenges, and they also reveal new scientific phenomena
and understanding. All modern portable electronics are enabled by the
materials in lithium ion batteries, which were developed in the 1980s,
which emphasises how just one materials class can transform how we
live: defining accelerated routes to new materials will open currently unimaginable technological possibilities for our future.

========================================================================== Story Source: Materials provided by University_of_Liverpool. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Andrij Vasylenko, Jacinthe Gamon, Benjamin B. Duff, Vladimir
V. Gusev,
Luke M. Daniels, Marco Zanella, J. Felix Shin, Paul M. Sharp,
Alexandra Morscher, Ruiyong Chen, Alex R. Neale, Laurence
J. Hardwick, John B.

Claridge, Fre'de'ric Blanc, Michael W. Gaultois, Matthew S. Dyer,
Matthew J. Rosseinsky. Element selection for crystalline inorganic
solid discovery guided by unsupervised machine learning of
experimentally explored chemistry. Nature Communications, 2021;
12 (1) DOI: 10.1038/ s41467-021-25343-7 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/09/210921081007.htm

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