News

Artificial intelligence helps to identify correct atomic structures

Bayesian inference enhanced structure search facilitates accurate detection of molecular adsorbate configurations.
Illustration showing how camphor adsorbs to a copper surface
The stable structures of camphor on Cu(111) are identified in the minima of the 6D adsorption energy surface, which is modeled as a function of molecular orientation and translations.

Functional materials are commonly employed in emerging technologies, such as green energy solutions and new electronic devices. These materials are typically blends of different organic and inorganic components and have many advantageous properties for novel applications. To achieve their full potential, we need precise knowledge on their atomic structure. State-of-the-art experimental tools, such as atomic force microscopy (AFM), can be used to investigate organic molecular adsorbates on metallic surfaces. However, interpreting the actual structure from microscopy images is often difficult. Computational simulations can help to estimate the most probable structures, but with complex materials, accurate structure search is computationally intractable with conventional methods. Recently, CEST group has developed new tools for automated structure prediction using machine learning algorithms from computer science.

In this most recent work, we demonstrate the accuracy and efficiency of our own Bayesian Optimization Structure Search (BOSS) artificial intelligence method. With BOSS, we identify the adsorbate configurations of a camphor molecule on a Cu(111) surface. This material has been previously studied with AFM, but inferring the structures from those images was inconclusive. Here, we show that BOSS can successfully identify not only the most probable structure, but also eight stable adsorbate configurations that camphor can have on Cu(111). We used these model structures to better interpret the AFM experiments and concluded that the images likely feature camphor which is chemically bound to the Cu surface via an oxygen atom. Analyzing single molecular adsorbates in this way is only the first step towards studying more complex assemblies of several molecules on surfaces and subsequently the formation of monolayers. The acquired insight on interface structures can help to optimize the functional properties of these materials.

More details can be found in the following publication:

J. Järvi, P. Rinke, and M. Todorović, Detecting Stable Adsorbates of (1S)-camphor on Cu(111) with Bayesian Optimization, Beilstein J. Nanotechnol.  2020, 11, 1577-1589. https://doi.org/10.3762/bjnano.11.140

  • Published:
  • Updated:
Share
URL copied!

Read more news

people exploring the walk-in closet that provides solutions for a sustainable fashion and textiles future
Cooperation, Research & Art, University Published:

'Wardrobe of the future' – sustainable solutions for the fashion and textile industry

The exhibition is showcasing solutions from researchers and students on sustainable future for the fashion and textile industry.
FinnFusion is a collaboration aiming to make fusion energy a reality. Photo: VTT.
Cooperation, Press releases, Research & Art Published:

Aalto Scientists Experiment with Helium Plasma to Help Pave Way for Fusion Energy

As part of FinnFusion, itself belonging to the EUROfusion consortium, Aalto scientists are experimenting with helium plasma to improve the future operations of Iter, an international fusion energy collaboration under construction in France.
Kuvassa näkyy Taloudenpuolustuskurssin puhujia keskustelemassa lavalla.
Research & Art Published:

Eighth Economic Defence Course digged into the topic of risk

For professors at the School of Business, times of radical uncertainty call for efforts to outline alternative scenarios and a critical perspective on talk of a ‘new era’.
Electronics - ICT Otaniemessä. Mikroelektroniikan suunnittelua.
Research & Art Published:

The EU funds a consortium with almost EUR 20 million in future radar research

Aalto University participates in the ARTURE consortium, which is funded by the European Defence Fund (EDF).