Autonomous robot plays with NanoLEGO
Scientists are developing an autonomous artificial intelligence system
that can selectively grip and move individual molecules

Date:
September 3, 2020
Source:
Forschungszentrum Juelich
Summary:
Atoms and molecules behave in a completely different way to
macroscopic objects and each brick requires its own 'instruction
manual'. Scientists have now developed an artificial intelligence
system that autonomously learns how to grip and move individual
molecules using a scanning tunneling microscope.



FULL STORY ========================================================================== Molecules are the building blocks of everyday life. Many materials are
composed of them, a little like a LEGO model consists of a multitude of different bricks. But while individual LEGO bricks can be simply shifted
or removed, this is not so easy in the nanoworld. Atoms and molecules
behave in a completely different way to macroscopic objects and each
brick requires its own "instruction manual." Scientists from Ju"lich
and Berlin have now developed an artificial intelligence system that autonomously learns how to grip and move individual molecules using a
scanning tunnelling microscope. The method, which has been published in
Science Advances, is not only relevant for research but also for novel production technologies such as molecular 3D printing.


========================================================================== Rapid prototyping, the fast and cost-effective production of prototypes or models -- better known as 3D printing -- has long since established itself
as an important tool for industry. "If this concept could be transferred
to the nanoscale to allow individual molecules to be specifically put
together or separated again just like LEGO bricks, the possibilities
would be almost endless, given that there are around 1060 conceivable
types of molecule," explains Dr. Christian Wagner, head of the ERC
working group on molecular manipulation at Forschungszentrum Ju"lich.

There is one problem, however. Although the scanning tunnelling
microscope is a useful tool for shifting individual molecules back
and forth, a special custom "recipe" is always required in order to
guide the tip of the microscope to arrange molecules spatially in a
targeted manner. This recipe can neither be calculated, nor deduced
by intuition -- the mechanics on the nanoscale are simply too variable
and complex. After all, the tip of the microscope is ultimately not a
flexible gripper, but rather a rigid cone. The molecules merely adhere
lightly to the microscope tip and can only be put in the right place
through sophisticated movement patterns.

"To date, such targeted movement of molecules has only been possible by
hand, through trial and error. But with the help of a self-learning,
autonomous software control system, we have now succeeded for the
first time in finding a solution for this diversity and variability
on the nanoscale, and in automating this process," says a delighted
Prof. Dr. Stefan Tautz, head of Ju"lich's Quantum Nanoscience institute.

The key to this development lies in so-called reinforcement learning,
a special variant of machine learning. "We do not prescribe a solution
pathway for the software agent, but rather reward success and penalize failure," explains Prof.

Dr. Klaus-Robert Mu"ller, head of the Machine Learning department at
TU Berlin.

The algorithm repeatedly tries to solve the task at hand and learns
from its experiences. The general public first became aware of
reinforcement learning a few years ago through AlphaGo Zero. This
artificial intelligence system autonomously developed strategies for
winning the highly complex game of Go without studying human players --
and after just a few days, it was able to beat professional Go players.

"In our case, the agent was given the task of removing individual
molecules from a layer in which they are held by a complex network of
chemical bonds. To be precise, these were perylene molecules, such
as those used in dyes and organic light-emitting diodes," explains
Dr. Christian Wagner. The special challenge here is that the force
required to move them must never exceed the strength of the bond with
which the tip of the scanning tunnelling microscope attracts the molecule, since this bond would otherwise break. "The microscope tip therefore
has to execute a special movement pattern, which we previously had to
discover by hand, quite literally," Wagner adds. While the software
agent initially performs completely random movement actions that break
the bond between the tip of the microscope and the molecule, over time
it develops rules as to which movement is the most promising for success
in which situation and therefore gets better with each cycle.

However, the use of reinforcement learning in the nanoscopic range brings
with it additional challenges. The metal atoms that make up the tip of
the scanning tunnelling microscope can end up shifting slightly, which
alters the bond strength to the molecule each time. "Every new attempt
makes the risk of a change and thus the breakage of the bond between tip
and molecule greater. The software agent is therefore forced to learn particularly quickly, since its experiences can become obsolete at any
time," Prof. Dr. Stefan Tautz explains.

"It's a little as if the road network, traffic laws, bodywork, and
rules for operating the vehicle are constantly changing while driving autonomously." The researchers have overcome this challenge by making
the software learn a simple model of the environment in which the
manipulation takes place in parallel with the initial cycles. The agent
then simultaneously trains both in reality and in its own model, which
has the effect of significantly accelerating the learning process.

"This is the first time ever that we have succeeded in bringing together artificial intelligence and nanotechnology," emphasizes Klaus-Robert
Mu"ller.

"Up until now, this has only been a 'proof of principle'," Tautz adds.

"However, we are confident that our work will pave the way for the robot- assisted automated construction of functional supramolecular structures,
such as molecular transistors, memory cells, or qubits -- with a speed, precision, and reliability far in excess of what is currently possible."

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


========================================================================== Journal Reference:
1. Philipp Leinen, Malte Esders, Kristof T. Schu"tt, Christian Wagner,
Klaus-Robert Mu"ller, F. Stefan Tautz. Autonomous robotic
nanofabrication with reinforcement learning. Science Advances,
2020; 6 (36): eabb6987 DOI: 10.1126/sciadv.abb6987 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/09/200903105557.htm

--- up 1 week, 3 days, 6 hours, 50 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1337:3/111)