Soft matter research has long been characterized by intrinsic complexity, arising from multiscale structure–property relationships, strong coupling between physical and chemical processes, and the need to reconcile theoretical abstraction with experimental observability. Computational tools have therefore become indispensable for navigating this complexity. From early algorithmic implementations developed for narrowly defined scientific problems, scientific software has evolved into sophisticated and often community-driven ecosystems. Despite these advances, persistent challenges remain in aligning rapidly evolving scientific demands with robust, generalizable, and sustainable software solutions.
This Research Topic brings together contributions that reflect the central role of scientific software in contemporary soft matter research. Rather than treating software as a secondary technical component, the articles collected here emphasize its function as an enabling research infrastructure—one that shapes how data is generated, curated, analyzed, and ultimately transformed into knowledge. Across diverse applications and methodological perspectives, the contributions highlight how progress in soft matter research increasingly depends on the integration of sound software engineering practices with domain-specific scientific insight.
A recurring theme across the Research Topic is the challenge of managing and reusing complex datasets generated across heterogeneous experimental and computational workflows. Exner et al. examine metadata stewardship in nanosafety research, identifying how project-centric data management practices have led to fragmentation and limited reuse. By advocating for flexible, machine-actionable metadata capture that can evolve alongside experimental workflows, their work reframes FAIR principles (Findable, Accessible, Interoperable, Reusable) as an integral part of day-to-day scientific practice rather than a post hoc compliance exercise. This perspective resonates strongly with the needs of soft matter research, where experiments, simulations, and models are tightly interwoven and rarely conform to rigid reporting templates.
Building on this foundation, Maier et al. present the NanoCommons Knowledge Base as a practical realization of a community-oriented approach to data and tool integration. Their contribution illustrates how semantic interoperability and programmatic access can transform disparate datasets and modeling tools into a coherent knowledge ecosystem. By emphasizing data “visiting” rather than duplication and by supporting both human and machine access, the NanoCommons infrastructure demonstrates how scientific software can function as a shared resource that outlives individual projects and adapts to evolving research questions.
In parallel with infrastructure-level developments, this Research Topic also highlights the importance of software that directly enables experimental investigation. Zakharov et al. introduce an open-source software package for synchronized control and acquisition of fluorescent signals, addressing common incompatibilities between hardware components and analysis tools. By lowering technical and financial barriers, their work exemplifies how thoughtfully designed scientific software can enhance experimental reproducibility, facilitate adoption across laboratories, and support a wide range of applications in cellular and soft matter research.
Looking toward the future, Cheimarios situates scientific software development within the broader transformation driven by artificial intelligence (AI). Focusing on soft matter physics as a demanding application domain, this review examines how machine-learned models, differentiable simulations, and automated pipelines are reshaping computational workflows. Importantly, the article emphasizes that these advances must be accompanied by lifecycle-oriented software practices, including reproducibility, provenance tracking, and governance frameworks. By integrating concepts from machine learning operations and FAIR principles for research software, this contribution provides a structured perspective on how AI-enabled scientific software can remain trustworthy, interpretable, and sustainable.
Collectively, the articles in this Research Topic underscore a broader shift in soft matter research: scientific software is no longer merely a vehicle for implementing models, but a central medium through which scientific understanding is constructed, validated, and shared. Whether addressing metadata stewardship, knowledge integration, experimental control, or AI-driven modeling, the contributions converge on the need for software systems that are flexible yet robust, specialized yet interoperable.
The challenges highlighted here—data fragmentation, reproducibility, scalability, and long-term sustainability—extend well beyond soft matter physics. As scientific inquiry becomes increasingly data-intensive and computationally adaptive, the approaches showcased in this Research Topic offer insights that are relevant across disciplines. By foregrounding scientific software as a core research output, this Research Topic aims to stimulate further dialogue and innovation at the intersection of physics, materials science, and software engineering, ultimately supporting more transparent, reproducible, and impactful science.
Statements
Author contributions
NC: Writing – original draft, Writing – review and editing. J-RH: Writing – review and editing, Writing – original draft. AA: Writing – review and editing, Writing – original draft. AL: Writing – original draft, Writing – review and editing. VL: Writing – review and editing, Writing – original draft. YD: Writing – review and editing, Writing – original draft.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
Author J-RH was employed by Materials Design SARL. Author AA was employed by NovaMechanics Ltd. and NovaMechanics Mike.
The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Summary
Keywords
computational workflows and interoperability, FAIR data and metadata stewardship, reproducibility and sustainability, scientific software development, soft matter research
Citation
Cheimarios N, Hill J-R, Afantitis A, Lyubartsev A, Lobaskin V and Dimakopoulos Y (2026) Editorial: Bridging IT and soft matter: challenges in scientific software development. Front. Phys. 14:1795202. doi: 10.3389/fphy.2026.1795202
Received
24 January 2026
Accepted
03 March 2026
Published
12 March 2026
Volume
14 - 2026
Edited and reviewed by
Erika Eiser, Norwegian University of Science and Technology, Norway
Updates
Copyright
© 2026 Cheimarios, Hill, Afantitis, Lyubartsev, Lobaskin and Dimakopoulos.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Nikolaos Cheimarios, nixeimar@chemeng.ntua.gr
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.