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 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 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 Topic offer insights that are relevant across disciplines. By foregrounding scientific software as a core research output, this collection 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.
Cheimarios et al. (Thu,) studied this question.