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.
Nikolaos Cheimarios (Fri,) studied this question.