Novel way to perform `general inverse design' with high accuracy

Date:
January 6, 2022
Source:
Singapore-MIT Alliance for Research and Technology (SMART)
Summary:
'Inverse design' is a design approach that reverses the traditional
design process and enables the designer to discover and create
materials that possess a user-defined set of properties. Researchers
demonstrate a nascent machine learning-based solution that uses
an algorithm to identify any material that exhibits specific
properties or characteristics. Termed 'general inverse design,'
the novel method is not limited to a particular set of elements or
crystal structure, but accesses all elements and crystal structures
and can design novel compounds different from known materials.



FULL STORY ========================================================================== Researchers from the Low Energy Electronic Systems (LEES)
Interdisciplinary Research Group (IRG) atSingapore-MIT Alliance for
Research and Technology (SMART), MIT's research enterprise in Singapore, together with collaborators at the Massachusetts Institute of Technology
(MIT), National University of Singapore (NUS) and Nanyang Technological University (NTU) have discovered a novel way to perform 'general inverse design' with reasonably high accuracy.

This breakthrough paves the way for further development of a burgeoning
and fast-moving field that could eventually enable the use of machine
learning to accurately identify materials based on a desired set of user-defined properties. This could be revolutionary for materials
science and have vast industrial benefits and use cases.


==========================================================================
A key challenge in materials science and research has been the
long-desired ability to create a material or compound with a specific
set of characteristics and properties in order to suit a particular
application or use case. To tackle this problem, researchers have
traditionally employed materials screening via materials-property
databases, which has led to the discovery of a limited number of
compounds with user-defined functional properties. However, even
with high-performance computing (HPC), the computational cost of the
necessary calculations is high, prohibiting an exhaustive search of the theoretical materials space. Consequently, there is a pressing need for an alternative method that can make this process of 'materials prospecting'
more comprehensive and efficient.

Enter inverse design. As the name suggests, the concept of inverse
design reverses the conventional design process, allowing new materials
and compounds to be 'reverse-engineered' simply by inputting a set of
desired properties and characteristics and then using an optimization
algorithm to generate a predicted solution. The recent advent of inverse
design has been of particular interest in the field of photonics, which
is increasingly turning to unconventional technologies to circumvent
inherent challenges associated with designing increasingly smaller yet
more powerful devices. Current methods involve traditional design, in
which a designer conceives of a fixed shape or structure as a starting
point. This process is labour-intensive and excludes a wide range of other devices with different shapes or structures from consideration, some of
which may have more potential than traditional shapes or structures.

Inverse design eliminates this problem and instead allows for
the fabrication of devices with the most optimal or effective
shape, structure, chemical composition, or other characteristics or
properties. While inverse design is not new, SMART researchers have taken
the technology a step further in their discovery of a viable method of 'general' inverse design, in which inverse design capability is not
limited to a particular set of elements or crystal structure, but is
able to access a diversity of elements and crystal structures.

This breakthrough is outlined in a paper titled, "An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties" recently published in the journal
Matter. In the research, the team demonstrates a framework for general
(both composition- and structure-varying) inverse design of inorganic
crystals, called FTCP (Fourier-Transformed Crystal Properties), that
allows for inverse design of crystals with user-specified properties
through sampling, decoding and post- processing. Even more promisingly,
the researchers show that FTCP is able to design new crystalline materials
that are dissimilar from known structures -- a significant development in
the exploration of this nascent technology with potentially revolutionary implications for materials science and industrial applications.

The algorithm developed by SMART researchers trains on more than 50,000 compounds in a materials database, then learns and generalises the
complex relationships between chemistry, structure, and properties in
order to predict novel compounds or materials that possess user-targeted characteristics. The algorithm predicts materials with target formation energies, bandgaps, and thermoelectric power factors, and validates
these predictions with simulations through density functional theory,
in turn demonstrating a reasonable degree of accuracy.

"This is an incredibly exciting development for the field of materials research. Materials science researchers now have an effective and
comprehensive tool that allows them to discover and create new compounds
and materials by simply inputting the desired characteristics," said Tonio Buonassisi, Principal Investigator at LEES and Professor of Mechanical Engineering at MIT.

Added S. Isaac P. Tian, NUS graduate student and co-first author on the
paper, "In the next step of this journey, an important milestone will
be to refine the algorithm to be able to better predict stability and manufacturability. These are exciting challenges that the SMART team
is currently working on with collaborators in Singapore and globally."
Zekun Ren, lead author and Postdoctoral Associate at LEES said, "The
aim of finding more effective and efficient ways to create materials
or compounds with user-defined properties has long been the focus of
materials science researchers. Our work demonstrates a viable solution
that goes beyond specialised inverse design, allowing researchers to
explore potential materials of varying composition and structure and
thus enabling the creation of a much wider range of compounds. This is
a pioneering example of successful general inverse design, and we hope
to build on this success in further research efforts." The research is
carried out by SMART and supported by the National Research Foundation
(NRF) Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.

========================================================================== Story Source: Materials provided by Singapore-MIT_Alliance_for_Research_and_Technology_ (SMART). Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Zekun Ren, Siyu Isaac Parker Tian, Juhwan Noh, Felipe Oviedo,
Guangzong
Xing, Jiali Li, Qiaohao Liang, Ruiming Zhu, Armin G. Aberle,
Shijing Sun, Xiaonan Wang, Yi Liu, Qianxiao Li, Senthilnath
Jayavelu, Kedar Hippalgaonkar, Yousung Jung, Tonio Buonassisi. An
invertible crystallographic representation for general inverse
design of inorganic crystals with targeted properties. Matter,
2022; 5 (1): 314 DOI: 10.1016/ j.matt.2021.11.032 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/01/220106143704.htm

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