Machine learning predicts nanoparticles' structure and dynamics

Date:
June 9, 2020
Source:
University of Jyva"skyla" - Jyva"skyla"n yliopisto
Summary:
Researchers have demonstrated that new distance-based machine
learning methods are capable of predicting structures and
atomic dynamics of nanoparticles reliably. The new methods are
significantly faster than traditional simulation methods used
for nanoparticle research and will facilitate more efficient
explorations of particle-particle reactions and particles'
functionality in their environment.



FULL STORY ========================================================================== Researchers at the Nanoscience Center and at the Faculty of Information Technology at the University of Jyva"skyla" in Finland have demonstrated
that new distance-based machine learning methods, developed in the
University of Jyva"skyla", are capable of predicting structures and atomic dynamics of nanoparticles reliably. The new methods are significantly
faster than traditional simulation methods used for nanoparticle research
and will facilitate more efficient explorations of particle-particle
reactions and particles' functionality in their environment. The study
was published in a Special Issue devoted to machine learning in The
Journal of Physical Chemistry on May 15, 2020.


==========================================================================
The new methods were applied to ligand-stabilized metal nanoparticles,
which have been long studied at the Nanoscience Center at the University
of Jyva"skyla". Last year, the researchers published a method that is
able to successfully predict binding sites of the stabilizing ligand
molecules on the nanoparticle surface. Now, a new tool was created that
can reliably predict potential energy based on the atomic structure
of the particle, without the need to use numerically heavy electronic
structure computations. The tool facilitates Monte Carlo simulations of
the atom dynamics of the particles at elevated temperatures.

Potential energy of a system is a fundamental quantity in computational nanoscience, since it allows for quantitative evaluations of system's stability, rates of chemical reactions and strengths of interatomic bonds.

Ligand-stabilized metal nanoparticles have many types of interatomic bonds
of varying chemical strength, and traditionally the energy evaluations
have been done by using the so-called density functional theory (DFT)
that often results in numerically heavy computations requiring the use
of supercomputers. This has precluded efficient simulations to understand nanoparticles' functionalities, e.g., as catalysts, or interactions with biological objects such as proteins, viruses, or DNA. Machine learning
methods, once trained to model the systems reliably, can speed up the simulations by several orders of magnitude.

The new method allowed simulations to be run on a laptop or desktop
In this work the researchers used the potential energies, predicted
by the machine learning method, to simulate the atomic dynamics of thiol-stabilized gold nanoparticles. The results were in good agreement
with the simulations performed by using the density functional theory. The
new method allowed simulations to be run on a laptop or desktop in a
time scale of a few hours while the reference DFT simulations took days
in a supercomputer and used simultaneously hundreds or even thousands
of computer cores. The speed-up will allow long-time simulations of
the particles' structural changes and particle- particle reactions at
elevated temperatures.

The researchers used a distance-based machine learning method developed
in the group of professor Tommi Ka"rkka"inen in Jyva"skyla". It describes
each momentary atomic configuration of a nanoparticle by calculating a so-called descriptor, and compares distances between descriptors in a multi-dimensional numerical space. By using correlations to a training
set created by the reference DFT simulations, the potential energy can
be predicted. This approach, used now for the first time in nanoparticle research, is simpler and more transparent than traditionally used neural networks.

"It is extremely motivating that we can reduce the computational load
from running simulations in supercomputers to running them with similar
quality in a laptop or a home PC," says PhD student Antti Pihlajama"ki
who is the lead author of the study.

"It was a great surprise that our relatively simple machine learning
methods work so well for complicated nanostructures," states professor
Tommi Ka"rkka"inen.

"In the next phase, our target is to generalize the method to work well
for nanoparticles of many different sizes and chemical compositions. We
will still need supercomputers to generate enough high-quality data to
train the machine learning algorithm, but we hope that in the future we
can move to use these new methods primarily to studies of nanoparticle functionality in complicated chemical environments," summarizes Academy Professor Hannu Ha"kkinen, who coordinated the study.


========================================================================== Story Source: Materials provided by University_of_Jyva"skyla"_-_Jyva"skyla"n_yliopisto. Note: Content may
be edited for style and length.


========================================================================== Journal Reference:
1. Antti Pihlajama"ki, Joonas Ha"ma"la"inen, Joakim Linja, Paavo
Nieminen,
Sami Malola, Tommi Ka"rkka"inen, Hannu Ha"kkinen. Monte Carlo
Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine
Learning Methods. The Journal of Physical Chemistry A, 2020; DOI:
10.1021/ acs.jpca.0c01512 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/06/200609104305.htm

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