2022 Impact factor 3.4


EPJ D Topical Issue: Dynamics of Systems on the Nanoscale (2021)

Guest Editors: Alexey V. Verkhovtsev, Vincenzo Guidi, Nigel J. Mason, Andrey V. Solov’yov

Understanding the Dynamics of Systems on the Nanoscale forms the core of a multidisciplinary research area addressing many challenging interdisciplinary problems at the interface of physics, chemistry, biology, and materials science. They include problems of structure formation, fusion and fission, collision and fragmentation, surfaces and interfaces, collective electronic excitations, reactivity, nanoscale phase and morphological transitions, irradiation driven transformations of complex molecular systems, biodamage, channeling phenomena, and many more. Common to these interdisciplinary scientific problems is the central role of the structure formation and dynamics of animate and inanimate matter on the nanometer scale.

This topical issue presents a collection of research papers devoted to different aspects of the Dynamics of Systems on the Nanoscale, ranging from fundamental research on elementary atomic and molecular mechanisms to studies at a more applied level, covering innovative theoretical, experimental and computational modeling techniques. Some of the contributions discuss specific applications of the research results in several modern and emerging technologies, such as controlled nanofabrication with charged particle beams or the design and practical realization of novel gamma-ray crystal-based light sources.


EPJ B Highlight - Investigating the use of noise to solve inverse physical problems

A graphical representation of a seismic inversion problem. Credit: Corso, et al, (2023)

New research looks at the problem of solving a physics problem starting with observational data and working backwards

The early success of physics comes mainly from solving direct or forward problems in which the physical state of a system can be described from a well-defined physical model and from governing equations. Yet, there exists a different type of problem, inverse problems, that are trickier to solve but are crucial to fields such as engineering, astrophysics and geophysics.

Solving these inverse problems requires taking a set of observational data and then working backwards, or inverting the problem, to arrive at the causal factors that gave rise to the data.

A new paper in EPJ B by Universidade Federal do Rio researchers Gilberto Corso and João Medeiros de Araujo, considers the possibility of solving inverse problems in physics by using statistical information from noise statistics.


EPJ B Highlight - Uncovering spin ladders in real compounds

Ladder in a low-dimensional spin system

Low-dimensional quantum systems named ‘spin ladders’ are strongly linked to superconductivity. A new theoretical approach has accurately predicted the nature of the spin ladder which appears in real chemical compound – possibly paving the way for new discoveries of advanced superconductors.

When fabricated in 1 or 2 dimensions, systems of particles whose quantum spins interact with each other can display some unique quantum properties. Through new research published in EPJ B, Asif Iqbal and Baidur Rahaman at Aliah University in Kolkata, India, developed a new theoretical technique for calculating the structures and interactions taking place in these unique materials. Their approach could pave the way for advanced new superconductors – which allow electric currents to flow through them with zero resistance.


EPJ E Highlight - Active Brownian particles have four distinct states of motion

Switching between locked and running states

Depending on the friction and external bias forces they experience, self-propelled Brownian particles will take on one of four possible states of motion. The discovery could help researchers to draw deeper insights into the behaviours of these unique systems in nature and technology.

Active Brownian motion describes particles which can propel themselves forwards, while still being subjected to random Brownian motions as they are jostled around by their neighbouring particles. Through new analysis published in EPJ E, Meng Su at Northwestern Polytechnical University in China, together with Benjamin Lindner at Humboldt University of Berlin, Germany, have discovered that these motions can be accurately described using four distinct mathematical patterns.


EPJ Data Science Highlight - Investigating gender equality in urban cycling

An overview of the gender gap in recreational cycling across cities included in the study according to Strava. Credit: A. Battison et al. (2023)

New research looks at why cycling has a low uptake among women in urban areas

Over recent years not only has cycling proved itself to be an outdoor activity with tremendous health benefits, but it has also presented itself as a useful tool in the quest to find an environmentally friendly method of urban transportation.

Despite the increasing popularity of cycling, many countries still have a negligible uptake in the pursuit and this is even more pronounced when considering how many women engage in cycling. To this day, a mostly unexplained gender gap exists in cycling.

A new paper in EPJ Data Science by the University of Turin Department of Computer Science researcher Alice Battiston and her co-authors attempts to understand the determinants behind the gender gap in cycling on a large scale.


EPJ E Highlight - Improving fluid simulations with embedded neural networks

Simulating flows in a complex fluid

While neural networks can help to improve the accuracy of fluid flow simulations, new research shows how their accuracy is limited unless the right approach is taken. By embedding fluid properties into neural networks, simulation accuracy can improve by orders of magnitude.

The Lattice Boltzmann Method (LBM) is a simulation technique used to describe the dynamics of fluids. Recently, there has been an increasing interest in employing neural networks for computational modelling of fluids. The results of a collaboration between researchers from Eindhoven University of Technology and Los Alamos National Laboratory, published in EPJ E, show how neural networks can be embedded into a LBM framework to model collisions between fluid particles. The team found that it is essential to embed the correct physical properties into the neural network architecture to preserve accuracy. These discoveries could deepen researchers’ understanding of how to model fluid flows.


EPJ D Highlight - Better understanding the bonds between carbon group elements

Experimental setup

Heating clusters of these elements reveals key differences

The bonds between clusters of elements in the fourteenth group of the periodic table are known to be fickle. Ranging from the nonmetal carbon, to the metalloids silicon and germanium, to the metals tin and lead, all these elements share the same configuration of valence electrons – electrons in their atoms’ outermost energy level. However, clusters formed from these elements respond differently to being excited with laser pulses. Studying the response of atomic clusters to photoexcitation as a function of the element they are composed of and their number of atoms reveals patterns that can be used to gain insight into their structure and binding mechanisms.


EPJ D Highlight - Predicting the composition of a steel alloy

Experimental setup

Austenitic steel is a potential material for nuclear fusion reactors

Producing energy on Earth through nuclear fusion, the type of reaction that powers the Sun, has proven to be a major challenge. The extreme conditions needed for such a reaction require the walls of a nuclear fusion device to be made of a material with a particular set of mechanical properties, including being able to withstand incredibly high temperatures and be shock- and corrosion-resistant. Austenitic steel, a non-magnetic steel with a crystalline structure, is one of the materials considered for use in nuclear fusion devices.


EPJ B Highlight - Statistical physics reveals how languages evolve

Charting the survival of linguistic structures

Models based on the principles of statistical physics can provide useful insights into how languages change through contact between speakers of different languages. In particular, the analysis reveals how unusual linguistic forms are more likely to be replaced by more regular ones over time.

The field of historical linguistics explores how languages change over time, with a particular focus on the evolution of sounds, meanings, and structures in words and sentences. So far, however, it hasn’t been widely studied from the viewpoint of statistical physics – which uses mathematical models to explain patterns and behaviours in complex, evolving systems. Through a series of models described in EPJ B, Jean-Marc Luck at Université Paris-Saclay, together with Anita Mehta at the Clarendon Institute in Oxford, use statistical physics to show how exceptions to well-established grammatical rules are linked to the influence of neighbouring languages.


EPJ E Highlight - Training models with a structured data curriculum

Building a structured curriculum of data

By carefully structuring the data used to train models of complex systems by leveraging physics and information theory, researchers can significantly improve the quality of their predictions, without relying on additional principles from machine learning in situations where less information about the system is available.

Researchers are now increasingly driven to identify and model the intricate mathematical patterns found in complex natural systems, where the interactions of many simple parts and subsystems can give rise to deeply intricate mathematical patterns. Today, machine learning is the most widely used technique to model these systems. Through new analysis in EPJ E, a research team at Université Paris-Saclay shows how a ‘curriculum learning’ approach, which carefully structures the data used to train models, can significantly improve their results, without relying on additional machine learning principles.


B. Fraboni and G. García López
Excellent work! We are very satisfied.

Juan Cruz Moreno, Instituto de Física La Plata, Argentinia

ISSN: 2190-5444 (Electronic Edition)

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