Soft Matter Lab

Technological Excellence Requires Human and Social Context on ArXiv

Why breakthrough research needs humanities and social sciences. *(From an artwork by Jacopo Sacquegno.)*

Technological Excellence Requires Human and Social Context
Karl Palmås, Mats Benner, Monica Billger, Ben Clarke, Raimund Feifel, Julia Fernandez-Rodriguez, Anna Foka, Juliette Griffié, Claes Gustafsson, Kerstin Hamilton, Johan Holmén, Kristina Lindström, Tobias Olofsson, Joana B. Pereira, Marisa Ponti, Julia Ravanis, Sviatlana Shashkova, Emma Sparr, Pontus Strimling, Fredrik Höök, Giovanni Volpe
arXiv: 2603.10653

Breakthrough technologies increasingly shape social institutions, economic systems, and political futures. Yet models of research excellence associated with such technologies often prioritize technical performance, scalability, and short-term innovation metrics while treating ethical, social, and cultural dimensions as secondary considerations. This perspective article argues that such separation is no longer tenable. We propose a broader understanding of excellence that combines technical rigor with ethical robustness, social intelligibility, and long-term relevance. The rapid emergence of generative and agentic artificial intelligence further underscores this argument. As technological systems increasingly operate through language, interpretation, and normative alignment, expertise traditionally cultivated in the humanities and social sciences becomes integral to the design, governance, and responsible deployment of such systems. Drawing on historical examples and contemporary research practices, this article examines five interconnected domains where the humanities and social sciences, treated as integrated dimensions of research practice, can strengthen technological development: (1) ethical, legal, and social integration in agenda-setting and research design; (2) plural and reflexive foresight practices that shape technological futures; (3) graduate education as a leverage point for cross-disciplinary literacy; (4) visualization and communication as epistemic and civic practices; and (5) institutional frameworks that move beyond rigid distinctions between basic and applied research. Across these dimensions, we propose practical strategies for embedding interdisciplinary collaboration structurally rather than symbolically.

Hari Prakash Thanabalan nailed his PhD thesis on March 5th, 2026

Hari nails his thesis. *(Image by A. Ciarlo.)*

Hari Prakash Thanabalan nailed his PhD thesis, Soft Robotic Platforms for Dynamic Conditions: From Adaptive Locomotion to Space Exploration, on March 5th, 2026.

The nailing took place in Universitetsbyggnaden i Vasaparken, Universitetsplatsen 1, Göteborg, at 14:00.

In Swedish academia, “nailing” (spikning) is the formal public announcement and publication of a doctoral thesis. It happens weeks before the defence so that the public has time to read the thesis in advance and prepare questions for the defence. In addition to the physical nailing, the thesis is also published electronically (e-spikning) via GUPEA.

Hari Prakash will defend his thesis on the 23rd of March 2026 at 13:00 in PJ Salen lecture hall, Institutionen för fysik, Johanneberg Campus, Göteborg.

Per Hillertz joins the Soft Matter Lab

Per Hillertz. *(Photo courtesy of P. Hillertz.)*

We are pleased to announce that Per Hillertz (AstraZeneca) joined the Soft Matter Lab on 26 February 2026 as Adjunct Professor.

Per brings a strong scientific background in structural biology and biophysics, with broad experience across multiple therapy areas, including Oncology, CNS-pain, and respiratory diseases. In his current role as IT Site Lead in Gothenburg and Director of M&A IT at AstraZeneca, he focuses on the development and application of AI technologies within the pharmaceutical value chain.
He also plays an active role in shaping academia–industry collaboration through his involvement in several program boards at Chalmers University of Technology and the University of Gothenburg.

We warmly welcome Per to the lab and look forward to strengthening our collaboration at the interface of soft matter, life sciences, and digital innovation.

Invited talk by S. K. Manikandan at The 15th Nordic Workshop on Statistical Physics, Nordita, 26 Feb 2026

Recent advances in nonequilibrium physics allow extracting thermodynamic quantities, such as entropy production, directly from dynamical information in microscopic movies. *(Figure by S. Manikandan, adapted from Manikandan et al., Phys. Rev. Research 6, 023310 (2024).)*

Localising Entropy Production and Maximally Dissipative Coordinates from Experimental Data
Sreekanth Manikandan
Date: 26th February 2026
Time: 14.15
Place: NORDITA, Stockholm, Sweden
The 15th Nordic Workshop on Statistical Physics: Biological, Complex and Non-equilibrium Systems

Identifying whether a process is in equilibrium, quantifying how far it lies from equilibrium, and determining optimal reduced descriptions of non-equilibrium processes remain challenging open problems. Here, we discuss how novel data-driven techniques grounded in stochastic thermodynamics can be used to efficiently learn these features directly from experimental data. In particular, we show how entropy production can be localized in space and time, and how maximally dissipative coordinates can be consistently inferred as effective low-dimensional descriptions of non-equilibrium processes. We further discuss applications to experimental biophysical systems and outline key challenges and limitations.

Photos

Sreekanth, presenting. *(Photo by A. Ciarlo)*

Invited talk by A. Ciarlo at The 15th Nordic Workshop on Statistical Physics, Nordita, 26 Feb 2026

Schematic illustration of the light-momentum detection principle underlying SmartTrap. The momentum change of the trapping laser, induced by its interaction with the trapped particle, is measured to directly quantify optical forces with high precision, enabling real-time feedback and autonomous control in non-equilibrium experiments. *(Figure by A. Ciarlo.)*

SmartTrap: Autonomous Optical Tweezers for Statistical Physics of Non-Equilibrium Systems
Antonio Ciarlo
Date: 26th February 2026
Time: 13.30
Place: NORDITA, Stockholm, Sweden
The 15th Nordic Workshop on Statistical Physics: Biological, Complex and Non-equilibrium Systems

Optical tweezers are a key tool in non-equilibrium statistical physics, allowing direct measurements of forces, work, and fluctuations in single-molecule and soft matter systems. However, manual operation limits throughput and the systematic study of rare events.

In this talk, Antonio Ciarlo will present SmartTrap, a fully autonomous optical tweezers platform integrating deep learning–based 3D tracking, adaptive feedback control, and automated microfluidics. The system operates without human intervention, executing complete force spectroscopy protocols.

Demonstrated with high-throughput DNA pulling experiments on λ-DNA, SmartTrap enables precise measurements of force–extension curves and folding kinetics. The platform also opens new possibilities for studies of colloids, single cells, and quantitative tests of non-equilibrium statistical physics.

Photos

Antonio, presenting. *(Photo by S. K. Manikandan)*

Invited lecture by A. Callegari, A. Ciarlo, and S. K. Manikandan at the Winter school on Geometry of nonequilibrium critical phenomena, Chalmers, 22-27 Feb 2026

Active Matter: Model Systems and Experimental Tests
Agnese Callegari, Antonio Ciarlo, Sreekanth Manikandan
Dates and times:
23 Feb 14:00-15:00 (Agnese)
24 Feb 11:30-12:30 (Antonio)
24 Feb 14:00-15:00 (Sreekanth)
Place: PJ
Winter school on Geometry of nonequilibrium critical phenomena

Active matter is a broad class of systems that operate intrinsically out of equilibrium. It spans multiple length scales—from macroscopic to micro- and nanoscopic—and includes both biological and artificial realizations, often displaying rich and emerging collective behaviors. The study of active matter aims to explain and interpret these phenomena using concepts and tools from physics. As such, understanding active and non-equilibrium systems requires a combination of theoretical, computational, and experimental approaches.

In the first part of the lecture, we introduce the concept of an active particle and demonstrate how it can be embodied in a macroscopic, self-propelled toy robot (a Hexbug). Despite their simplicity, such systems reproduce characteristic—and sometimes counterintuitive—features of microscopic active matter. These experiments have a strong pedagogical value and are designed to help bridge a gap in traditional physics curricula at the primary and secondary education levels.

The second part of the lecture focuses on active matter and non-equilibrium phenomena at the microscopic scale, where advanced experimental tools are essential. Optical tweezers provide precise control over microscopic systems and access to key physical observables. We introduce their operating principles and illustrate how they can be used to construct a minimal, well-controlled experimental model for studying non-equilibrium dynamics at the single-particle level.

In the final part of the lecture, we turn to the theoretical and computational tools required to analyze active matter systems. We discuss how non-equilibrium dynamics can be quantitatively characterized directly from experimental data in a model-independent framework. This naturally leads to an introduction to machine-learning–based inference techniques, which extract dynamical and thermodynamic information from data without relying on a priori assumptions about the underlying physical model.

References:
[1] A. Barona Balda, A. Argun, A. Callegari, G. Volpe. Playing with Active Matter, Am. J. Phys. 92, 847–858 (2024). https://doi.org/10.1119/5.0125111
[2] Martins, T.T., Malavazi, A.H.A., Kamizaki, L.P. et al. Fluctuation theorems with optical tweezers: theory and practice. Eur. Phys. J. Plus 141, 71 (2026). https://doi.org/10.1140/epjp/s13360-025-07181-4
[3] Manikandan, Sreekanth K. and Ghosh, T. and Mandal, T. and Biswas, A. and Sinha, B. and Mitra, D. Estimate of entropy production rate can spatiotemporally resolve the active nature of cell flickering. Phys. Rev. Res. 6, 023310 (2024). https://doi.org/10.1103/PhysRevResearch.6.023310

Photos

Antonio, presenting. *(Photo by M. Orsino)*

Anton Widengård joins the Soft Matter Lab

Anton Widengård joined the Soft Matter Lab on 9 February 2026.

Anton is a master student in Biomedical Engineering at Chalmers University of Technology.

Alongside with his classmate Max Haraldsson, Anton will be working on his Master’s Thesis at Soft Matter Lab, in collaboration with IFLAI, supervised by Jesús Pineda.

Anton’s and Max’s research focuses on evaluating and efficiently adapting pre-trained deep-learning vision models for cell segmentation and tracking.

Max Haraldsson joins the Soft Matter Lab

Max Haraldsson joined the Soft Matter Lab on 9 February 2026.

Max is a master student in Biomedical Engineering at Chalmers University of Technology.

Together with his classmate Anton Widengård, he will be conducting his Master’s thesis at the Soft Matter Lab in collaboration with IFLAI, with Jesús Pineda as supervisor.

Max’s and Anton’s project is about evaluating and efficiently adapting pre-trained deep learning models for cell segmentation and tracking.

Fredrik Skärberg defended his PhD thesis on January 29th, 2026. Congrats!

Cover of the PhD thesis. *(Image by F. Skärberg)*

Fredrik Skärberg defended his PhD thesis on January 29th, 2026. Congrats!
The defense took place in FB, Institutionen för fysik, Origovägen 6b, Göteborg, at 09:00.

Title: From Light to Data Using Deep Learning for Quantitative Microscopy

Abstract: Quantitative microscopy aims to measure physical properties of microscopic particles from optical images, but weak and complex signals often make this difficult. This thesis explores how computational methods, especially deep learning guided by physical understanding, can improve particle detection and characterization in microscopy.
The work introduces new approaches for locating and tracking particles, extends these ideas to three-dimensional and label-free imaging, and reviews practical analysis workflows. It further shows how combining complementary imaging techniques can enhance nanoparticle measurements and how deep learning can recover three-dimensional structural information from microscopy images.
Overall, this thesis strengthens the connection between optical measurements and quantitative particle information, expanding the potential of label-free microscopy for biological and nanoscale studies.

Thesis: https://gupea.ub.gu.se/handle/2077/90201

Supervisor: Daniel Midtvedt
Examiner: Raimund Feifel
Opponent: Arrate Munoz Barrutia
Committee: Per Augustsson, Jens Petersen, Rebecka Jörnsten
Alternate board member: Vitali Zhaunerchyk

Roadmap on Deep Learning for Microscopy published in Journal of Physics: Photonics

Spatio-temporal spectrum diagram of microscopy techniques and their applications. *(Image by the Authors of the manuscript.)*

Roadmap on Deep Learning for Microscopy
Giovanni Volpe, Carolina Wählby, Lei Tian, Michael Hecht, Artur Yakimovich, Kristina Monakhova, Laura Waller, Ivo F. Sbalzarini, Christopher A. Metzler, Mingyang Xie, Kevin Zhang, Isaac C.D. Lenton, Halina Rubinsztein-Dunlop, Daniel Brunner, Bijie Bai, Aydogan Ozcan, Daniel Midtvedt, Hao Wang, Nataša Sladoje, Joakim Lindblad, Jason T. Smith, Marien Ochoa, Margarida Barroso, Xavier Intes, Tong Qiu, Li-Yu Yu, Sixian You, Yongtao Liu, Maxim A. Ziatdinov, Sergei V. Kalinin, Arlo Sheridan, Uri Manor, Elias Nehme, Ofri Goldenberg, Yoav Shechtman, Henrik K. Moberg, Christoph Langhammer, Barbora Špačková, Saga Helgadottir, Benjamin Midtvedt, Aykut Argun, Tobias Thalheim, Frank Cichos, Stefano Bo, Lars Hubatsch, Jesus Pineda, Carlo Manzo, Harshith Bachimanchi, Erik Selander, Antoni Homs-Corbera, Martin Fränzl, Kevin de Haan, Yair Rivenson, Zofia Korczak, Caroline Beck Adiels, Mite Mijalkov, Dániel Veréb, Yu-Wei Chang, Joana B. Pereira, Damian Matuszewski, Gustaf Kylberg, Ida-Maria Sintorn, Juan C. Caicedo, Beth A Cimini, Muyinatu A. Lediju Bell, Bruno M. Saraiva, Guillaume Jacquemet, Ricardo Henriques, Wei Ouyang, Trang Le, Estibaliz Gómez-de-Mariscal, Daniel Sage, Arrate Muñoz-Barrutia, Ebba Josefson Lindqvist, Johanna Bergman
Journal of Physics: Photonics 8, 012501 (2026)
arXiv: 2303.03793
doi: 10.1088/2515-7647/ae0fd1

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning (ML) are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap encompasses key aspects of how ML is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of ML for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.