Soft Matter Lab presentations at the SPIE Optics+Photonics Digital Forum

Seven members of the Soft Matter Lab (Saga HelgadottirBenjamin Midtvedt, Aykut Argun, Laura Pérez-GarciaDaniel MidtvedtHarshith BachimanchiEmiliano Gómez) were selected for oral and poster presentations at the SPIE Optics+Photonics Digital Forum, August 24-28, 2020.

The SPIE digital forum is a free, online only event.
The registration for the Digital Forum includes access to all presentations and proceedings.

The Soft Matter Lab contributions are part of the SPIE Nanoscience + Engineering conferences, namely the conference on Emerging Topics in Artificial Intelligence 2020 and the conference on Optical Trapping and Optical Micromanipulation XVII.

The contributions being presented are listed below, including also the presentations co-authored by Giovanni Volpe.

Note: the presentation times are indicated according to PDT (Pacific Daylight Time) (GMT-7)

Emerging Topics in Artificial Intelligence 2020

Saga Helgadottir
Digital video microscopy with deep learning (Invited Paper)
26 August 2020, 10:30 AM
SPIE Link: here.

Aykut Argun
Calibration of force fields using recurrent neural networks
26 August 2020, 8:30 AM
SPIE Link: here.

Laura Pérez-García
Deep-learning enhanced light-sheet microscopy
25 August 2020, 9:10 AM
SPIE Link: here.

Daniel Midtvedt
Holographic characterization of subwavelength particles enhanced by deep learning
24 August 2020, 2:40 PM
SPIE Link: here.

Benjamin Midtvedt
DeepTrack: A comprehensive deep learning framework for digital microscopy
26 August 2020, 11:40 AM
SPIE Link: here.

Gorka Muñoz-Gil
The anomalous diffusion challenge: Single trajectory characterisation as a competition
26 August 2020, 12:00 PM
SPIE Link: here.

Meera Srikrishna
Brain tissue segmentation using U-Nets in cranial CT scans
25 August 2020, 2:00 PM
SPIE Link: here.

Juan S. Sierra
Automated corneal endothelium image segmentation in the presence of cornea guttata via convolutional neural networks
26 August 2020, 11:50 AM
SPIE Link: here.

Harshith Bachimanchi
Digital holographic microscopy driven by deep learning: A study on marine planktons (Poster)
24 August 2020, 5:30 PM
SPIE Link: here.

Emiliano Gómez
BRAPH 2.0: Software for the analysis of brain connectivity with graph theory (Poster)
24 August 2020, 5:30 PM
SPIE Link: here.

Optical Trapping and Optical Micromanipulation XVII

Laura Pérez-García
Reconstructing complex force fields with optical tweezers
24 August 2020, 5:00 PM
SPIE Link: here.

Alejandro V. Arzola
Direct visualization of the spin-orbit angular momentum conversion in optical trapping
25 August 2020, 10:40 AM
SPIE Link: here.

Isaac Lenton
Illuminating the complex behaviour of particles in optical traps with machine learning
26 August 2020, 9:10 AM
SPIE Link: here.

Fatemeh Kalantarifard
Optical trapping of microparticles and yeast cells at ultra-low intensity by intracavity nonlinear feedback forces
24 August 2020, 11:10 AM
SPIE Link: here.

Note: the presentation times are indicated according to PDT (Pacific Daylight Time) (GMT-7)

Machine learning reveals complex behaviours in optically trapped particles published in Machine Learning: Science and Technology

Illustration of a fully connected neural network with three inputs, three outputs, and three hidden layers.

Machine learning reveals complex behaviours in optically trapped particles
Isaac C. D. Lenton, Giovanni Volpe, Alexander B. Stilgoe, Timo A. Nieminen & Halina Rubinsztein-Dunlop
Machine Learning: Science and Technology, 1 045009 (2020)
doi: 10.1088/2632-2153/abae76
arXiv: 2004.08264

Since their invention in the 1980s, optical tweezers have found a wide range of applications, from biophotonics and mechanobiology to microscopy and optomechanics. Simulations of the motion of microscopic particles held by optical tweezers are often required to explore complex phenomena and to interpret experimental data. For the sake of computational efficiency, these simulations usually model the optical tweezers as an harmonic potential. However, more physically-accurate optical-scattering models are required to accurately model more onerous systems; this is especially true for optical traps generated with complex fields. Although accurate, these models tend to be prohibitively slow for problems with more than one or two degrees of freedom (DoF), which has limited their broad adoption. Here, we demonstrate that machine learning permits one to combine the speed of the harmonic model with the accuracy of optical-scattering models. Specifically, we show that a neural network can be trained to rapidly and accurately predict the optical forces acting on a microscopic particle. We demonstrate the utility of this approach on two phenomena that are prohibitively slow to accurately simulate otherwise: the escape dynamics of swelling microparticles in an optical trap, and the rotation rates of particles in a superposition of beams with opposite orbital angular momenta. Thanks to its high speed and accuracy, this method can greatly enhance the range of phenomena that can be efficiently simulated and studied.