'Self-driving' microscopes discover shortcuts to new materials

Date:
May 9, 2022
Source:
DOE/Oak Ridge National Laboratory
Summary:
Researchers are teaching microscopes to drive discoveries with an
intuitive algorithm that could guide breakthroughs in new materials
for energy technologies, sensing and computing.



FULL STORY ========================================================================== Researchers at the Department of Energy's Oak Ridge National Laboratory
are teaching microscopes to drive discoveries with an intuitive algorithm, developed at the lab's Center for Nanophase Materials Sciences, that
could guide breakthroughs in new materials for energy technologies,
sensing and computing.


========================================================================== "There are so many potential materials, some of which we cannot study
at all with conventional tools, that need more efficient and systematic approaches to design and synthesize," said Maxim Ziatdinov of ORNL's Computational Sciences and Engineering Division and the CNMS. "We can
use smart automation to access unexplored materials as well as create
a shareable, reproducible path to discoveries that have not previously
been possible." The approach, published in Nature Machine Intelligence, combines physics and machine learning to automate microscopy experiments designed to study materials' functional properties at the nanoscale.

Functional materials are responsive to stimuli such as heat or electricity
and are engineered to support both everyday and emerging technologies,
ranging from computers and solar cells to artificial muscles and
shape-memory materials.

Their unique properties are tied to atomic structures and microstructures
that can be observed with advanced microscopy. However, the challenge
has been to develop efficient ways to locate regions of interest where
these properties emerge and can be investigated.

Scanning probe microscopy is an essential tool for exploring the
structure- property relationships in functional materials. Instruments
scan the surface of materials with an atomically sharp probe to
map out the structure at the nanometer scale -- the length of one
billionth of a meter. They can also detect responses to a range of
stimuli, providing insights into fundamental mechanisms of polarization switching, electrochemical reactivity, plastic deformation or quantum phenomena. Today's microscopes can perform a point-by-point scan of a
nanometer square grid, but the process can be painstakingly slow, with measurements collected over days for a single material.

"The interesting physical phenomena are often only manifested in a small
number of spatial locations and tied to specific but unknown structural elements.

While we typically have an idea of what will be the characteristic
features of physical phenomena we aim to discover, pinpointing these
regions of interest efficiently is a major bottleneck," said former ORNL
CNMS scientist and lead author Sergei Kalinin, now at the University
of Tennessee, Knoxville. "Our goal is to teach microscopes to seek
regions with interesting physics actively and in a manner much more
efficient than performing a grid search." Scientists have turned to
machine learning and artificial intelligence to overcome this challenge,
but conventional algorithms require large, human-coded datasets and may
not save time in the end.



==========================================================================
For a smarter approach to automation, the ORNL workflow incorporates
human- based physical reasoning into machine learning methods and uses
very small datasets -- images acquired from less than 1% of the sample
-- as a starting point. The algorithm selects points of interest based
on what it learns within the experiment and on knowledge from outside
the experiment.

As a proof of concept, a workflow was demonstrated using scanning
probe microscopy and applied to well-studied ferroelectric
materials. Ferroelectrics are functional materials with a reorientable
surface charge that can be leveraged for computing, actuation and sensing applications. Scientists are interested in understanding the link between
the amount of energy or information these materials can store and the
local domain structure governing this property. The automated experiment discovered the specific topological defects for which these parameters
are optimized.

"The takeaway is that the workflow was applied to material systems
familiar to the research community and made a fundamental finding,
something not previously known, very quickly -- in this case, within a
few hours," Ziatdinov said.

Results were faster -- by orders of magnitude -- than conventional
workflows and represent a new direction in smart automation.

"We wanted to move away from training computers exclusively on data
from previous experiments and instead teach computers how to think
like researchers and learn on the fly," said Ziatdinov. "Our approach
is inspired by human intuition and recognizes that many material
discoveries have been made through the trial and error of researchers
who rely on their expertise and experience to guess where to look."
ORNL's Yongtao Liu was responsible for the technical challenge of getting
the algorithm to run on an operational microscope at the CNMS. "This is
not an off- the-shelf capability, and a lot of work goes into connecting
the hardware and software," said Liu. "We focused on scanning probe
microscopy, but the setup can be applied to other experimental imaging
and spectroscopy approaches accessible to the broader user community."
The journal article is published as "Experimental discovery of structure- property relationships in ferroelectric materials via active learning."
The work was supported by the CNMS, which is a DOE Office of Science
user facility, and the Center for 3D Ferroelectric Microelectronics,
which is an Energy Frontier Research Center led by Pennsylvania State University and supported by the DOE Office of Science.


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


========================================================================== Journal Reference:
1. Yongtao Liu, Kyle P. Kelley, Rama K. Vasudevan, Hiroshi Funakubo,
Maxim
A. Ziatdinov, Sergei V. Kalinin. Experimental discovery of
structure- property relationships in ferroelectric materials via
active learning.

Nature Machine Intelligence, 2022; 4 (4): 341 DOI:
10.1038/s42256-022- 00460-0 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/05/220509150750.htm

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