Presentation by D. Bronte Ciriza at OSA-OMA-2021

Optical forces calculated on a sphere with the geometrical optics (left column) and the machine learning (center column) approaches. The difference between both approaches is shown in the column on the right, illustrating the removal of artefacts with the machine learning method.
Machine learning to enhance the calculation of optical forces in the geometrical optics approximation
David Bronte Ciriza, Alessandro Magazzù, Agnese Callegari, Maria A. Iatì, Giovanni Volpe, Onofrio M. Maragò
Submitted to: OSA-OMA-2021, AF2D.2 Contribution
Date: 16 April
Time: 17:00 CEST

Short Abstract:
We show how machine learning can improve the speed and accuracy of the optical force calculations in the geometrical optics approximation

Extended Abstract:
Light can exert forces by exchanging momentum with particles. Since the pioneering work by Ashkin in the 1970’s, optical forces have played a fundamental role in fields like biology, nanotechnology, or atomic physics. Optical tweezers, which are instruments that, by tightly focusing a laser beam, are capable of confining particles in three dimensions, have become a common tool for manipulation of micro- and nano- particles, as well as a force and torque transducer with sensing capabilities at the femtonewton level. Optical tweezers have also been successfully employed to explore novel phenomena, including protein folding and molecular motors, or the optical forces and Brownian motion of 1D and 2D materials.

Numerical simulations play a fundamental role in the planning of experiments and in the interpretation of the results. In some basic cases for optical tweezers, the optical trap can be approximated by a harmonic potential. However, there are many situations where this approximation is insufficient, for example in the case of a particle escaping an optical trap, or for particles that are moving on an optical landscape but are not trapped. In these cases, a more complex treatment of the light-matter interaction is required for a more accurate calculation of the forces. This calculation is computationally expensive and prohibitively slow for numerical simulations when the forces need to be calculated many times in a sequential way. Recently, machine learning has been demonstrated to be a promising approach to improve the speed of these calculations and therefore, to expand the applicability of numerical simulations for experimental design and analysis.

In this work, we explore the geometrical optics regime, valid when the particles are significantly bigger than the wavelength of the incident light. This is typically the case in experiments with micrometer-size particles. The optical field is described by a collection of N light rays and the momentum exchange between the rays and the particle is calculated employing the tools of geometrical optics. The limitation of considering a discrete N number of light rays introduces artifacts in the force calculation. We show that machine learning can be used to improve not only the speed but also the accuracy of the force calculation. This is first demonstrated by training a neural network for the case of a spherical particle with 3 degrees of freedom accounting for the position of the particle. We show how the neural network improves the prediction of the force with respect to the initial training data that has been generated through the geometrical optics approach.

Starting from these results for 3 degrees of freedom, the work has been expanded to 9 degrees of freedom by including all the relevant parameters for the calculation of the optical forces considering also different refractive indexes, shapes, sizes, positions, and orientations of the particle besides different numerical apertures of the objective that focuses the light.

This work proves machine learning as a compact, accurate, and fast approach for optical forces calculation and presents a tool that can be used to study systems that, due to computation limitations, were out of the scope of the traditional ray optics approach.

Presentation by Liam Ruske at CECAM Mixed-Gen and Fundamentals of Growing Active Matter Workshop

3D droplets composed of active matter change their shape in response to a continuous influx of energy. Active droplets display an unprecedented range of complex morphologies, from cup-shaped droplet invagination, run-and-tumble motion or surface wrinkles caused by contractile activity, to the continuous formation and retraction of finger-like protrusions driven by extensile activity.
Liam Ruske has taken the opportunity to present and discuss his work on three-dimensional organisation and morphology of active droplets at the CECAM Mixed-Gen series on March 4 and the Fundamental of Growing Active Matter workshop on March 25.

A lot is understood about the ways in which single cells move, but there are still many questions about the motion and organisation of cell aggregates where cells coupled through intercellular junctions show a range of collective behaviours.

This work, which has been recently published Phys. Rev. X 11, 021001 (2021), shows the potential of active nematic continuum models to describe collective cell motion in a three dimensional environment.

Popular Summary:

Active matter describes systems—living and synthetic—where a continuous influx of energy at the level of individual components leads to striking collective behavior among the individual components, such as self-organizing bacteria colonies, bird flocks, or polymers in the cytoskeleton of cells. Understanding their behavior has attracted interest for studies of biological systems—from the spread of cancer to the development of organisms—as well the development of mesoscopic engines. Here, we numerically investigate 3D droplets composed of active matter and the ways in which their shapes change in response to the continuous input of energy.

One striking observation is the continuous formation of fingerlike protrusions, reminiscent of the collective motion of invading cancer cells. By changing the mechanical properties of the drop or the activity level, we find several different dynamical responses: For example, the droplet surface can wrinkle in a way that resembles a walnut or the active forces can drive a dimple in the droplet to grow, leading to a cup shape. Such invagination is reminiscent of patterns seen during morphogenesis.

Understanding the behavior of model systems, here a continuum model of active material, is an important step toward the goal of understanding the role of physical theories in the life sciences.

Ayten Gülce Bayram presents her PhD project at the ActiveMatter online meeting, 10 September 2020

The first meeting between all PIs and ESRs meeting in our network took place on 10 September 2020. During this meeting, Ayten Gülce Bayram, ESR from Bilkent University, presented herself and her research project through a short video presentation. If you are curious about how her research studies are going as a first-year doctoral student in ActiveMatter ITN, please have a look at her presentation!

Ayten Gülce BAYRAM, ESR from Bilkent University, presents herself and her research on the modeling crystallization of active colloidal suspensions.

Laura Natali presents her PhD project at the ActiveMatter online meeting, 10 September 2020

The ActiveMatter ESRs + PIs Online Meeting (if you want to know more read the main news here) took place on the 10th of last month. For the first time, the members of the ActiveMatter were in the “same place” also if just online. All the Early Stage Researchers were asked to introduce themselves in a short presentation video.

Laura Natali, ESR at the University of Gothenburg, also presented herself during the meeting.

The video lasts only five minutes and introduces Laura, her current project and the area of research she will study during the PhD. You can find the presentation below, and it is also on the ActiveMatter youtube channel .

Liam Ruske presents his PhD project at the ActiveMatter online meeting, 10 September 2020

The first major meeting between the ESRs and PIs in our network took place on 10 September. On that occasion Liam Ruske, ESR from the University of Oxford, gave a brief introduction to the field of active fluids in the form of a short presentation.

Why not take a moment to learn about why active liquid crystals surprisingly exhibit turbulence at small Reynolds numbers and how the study of active nematics can help us to better understand collective dynamics in biological systems.

Chun-Jen Chen presents his PhD project at the ActiveMatter online meeting, 10 September 2020

As our first event with full participation of both PIs and ESRs took place on the 10th of September 2020, all ESRs had a chance to introduce their PhD projects, and benefit from the collective discussion and feedback from the other members of the network.

Chun-Jen Chen (UKONS) briefly explained the Active Brownian Colloidal (ABC) system with real-time controls to displacements and orientations of each individual ABC particles. He further demonstrated the application of such ABC system in the investigation of spontaneous collective behaviours of living systems (also more detail here by UKONS), and how he would extend the study to higher non-equilibrium and more stimulus-interactive cases, e.g. collective prey-predator interactions.

Chun-Jen presentation is published along with all other ESR presentations on the network’s Youtube channel. You can also read about the meeting and get the full list of ESR presentation videos in the Event post.

David Bronte Ciriza presents his PhD project at the ActiveMatter online meeting, 10 September 2020

On the 10th of September, the first online meeting between all the ESRs and PIs took place. During this meeting, David presented himself and introduced his future work at the CNR by means of this short video. The presentation was followed by time for questions and discussion with other members of the ActiveMatter network.

Are you wondering about what David did before joining the network? Do you want to know a little bit more about his project? Take a look at his presentation video!

David, ESR at the CNR, presents himself and introduces his work on the study of elongated active particles through optical forces.

Jesús Manuel Antunez Dominguez presents her PhD project at the ActiveMatter online meeting, 10 September 2020

The first meeting of the Active Matter ITN consortium, including ESRs and PIs, took place virtually on the 10th September. Each ActiveMatter project was briefly introduced through a video presentation. Here you can find the video presentation by Jesús Manuel, a PhD student at Elvesys in collaboration with the University of Gothenburg. This project merges industrial and academic research in order to unveil complex systems like soil through microfluidics.

Carolina van Baalen presents her PhD project at the ActiveMatter online meeting, 10 September 2020

On September the 10th the first meeting between all ESRs and PIs in our network took place. During this meeting Carolina van Baalen, ESR from ETH Zurich, presented herself and her project by means of a short video. Curious what her work looks like as a first year doctoral student in Active Matter? Have a look at her video, in which she will give you a glimpse of her research on active colloids at liquid interfaces!

Carolina van Baalen, ESR from ETH Zurich, presents herself and her work on active colloids at liquid interfaces.