REVIEW article
Front. Phys.
Sec. Soft Matter Physics
This article is part of the Research TopicBridging IT and Soft Matter: Challenges in Scientific Software DevelopmentView all 4 articles
Scientific Software Development in the AI Era: Reproducibility, MLOps, and Applications in Soft Matter Physics
Provisionally accepted
- 1Accenture (Ireland), Dublin, Ireland
- 2Ethniko Metsobio Polytechneio Schole Chemikon Mechanikon, Athens, Greece
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Artificial intelligence (AI) is redefining the foundations of scientific software development by turning once-static codes into dynamic, data-dependent systems that require continuous retraining, monitoring, and governance. This article offers a practitioner-oriented synthesis for building reproducible, sustainable, and trustworthy scientific software in the AI era, with a focus on soft matter physics as a demanding yet fertile proving ground. We examine advances in machine-learned interatomic and coarse-grained potentials, differentiable simulation engines, and closed-loop inverse design strategies, emphasizing how these methods transform modeling workflows from exploratory simulations into adaptive, end-to-end pipelines. Drawing from software engineering and MLOps, we outline lifecycle-oriented practices for reproducibility, including containerized environments, declarative workflows, dataset versioning, and model registries with FAIR-compliant metadata. Governance frameworks such as the NIST AI Risk Management Framework and the EU AI Act are discussed as critical scaffolding for risk assessment, transparency, and auditability. By integrating these engineering and scientific perspectives, we propose a structured blueprint for AI-driven modeling stacks that can deliver scalable, verifiable, and regulatory-ready scientific results. This work positions soft matter physics not just as a beneficiary of AI but as a key testbed for shaping robust, reproducible, and accountable computational science.
Keywords: Artificial Intelligence in Science, Scientific Software Engineering, Machine Learning Operations (MLOps), Reproducibility and Provenance, Soft Matter Modeling, differentiable simulation, Physics-informed machine learning, FAIR Principles for Research Software
Received: 23 Sep 2025; Accepted: 11 Nov 2025.
Copyright: © 2025 Cheimarios. 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) or licensor 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.