Foundation models in scientific research are large-scale artificial intelligence systems trained on vast, heterogeneous datasets and fine-tuned to support multiple analytical and modeling tasks. Unlike narrow AI tools built for single-purpose workflows, foundation models can operate across domains, interpret complex data relationships, and support diverse research applications. A new report from the National Academies of Sciences, Engineering, and Medicine examines how the US Department of Energy (DOE) could integrate foundation models with traditional computational modeling methods to accelerate discovery across materials science, Earth systems research, and high-performance computing environments.
For laboratory management, this emerging trend signals potential changes in data governance, automation oversight, workforce skills, and validation practices as hybrid AI–physics modeling becomes more common in scientific workflows.
Foundation models in scientific research and traditional computational modeling
The report emphasizes that foundation models in scientific research should complement, rather than replace, the trusted computational modeling frameworks that underpin predictive science. Traditional models are grounded in physical laws, extensively verified and validated, and essential for safety-critical research areas such as nuclear systems and complex materials analysis. The committee recommends a synergistic approach that combines the interpretive strengths of artificial intelligence with the reliability of established computational modeling systems.
“Foundation models hold great promise for scientific discovery, even as their nascent stage of development means they carry limitations and challenges,” said Dona Crawford, retired associate director for computation at Lawrence Livermore National Laboratory and chair of the committee that wrote the report. This integrated approach may enable laboratories to analyze larger and more diverse datasets while maintaining scientific rigor, provided uncertainty quantification, documentation, and assurance practices continue to evolve alongside AI-enabled research methods.
Data governance, infrastructure, and reproducibility in laboratory management
As foundation models in scientific research expand, laboratories engaged in data-intensive or DOE–aligned projects may need to strengthen data architecture, metadata standards, and stewardship practices. Operational priorities may include secure storage and access controls, high-performance or cloud-computing strategies, and clearer documentation, audit trails, and reproducibility protocols for AI-assisted workflows. The report also recommends standardized benchmarks for training and reproducing scientific foundation models, which may influence expectations for data integrity and accountability across collaborating research laboratories.
Automation, autonomous systems, and safety oversight in laboratory environments
The committee identifies opportunities to apply autonomous artificial intelligence systems to support laboratory operations and computational modeling activities. For laboratory management, this introduces responsibilities related to safety oversight, human-in-the-loop decision-making controls, change management and standard operating procedure (SOP) development, technical training requirements, and alignment with accreditation and quality frameworks. While AI-enabled automation can expand analytical capability, it also increases the importance of transparency, risk assessment, and traceable model behavior within laboratory environments.
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Workforce skills and interdisciplinary collaboration in hybrid modeling ecosystems
Hybrid AI and computational modeling environments require interdisciplinary expertise that spans data science, machine learning, domain-specific scientific knowledge, and validation and quality management practices. Laboratory management teams may see growing demand for cross-trained staff, new collaboration structures, and broader computational modeling literacy as foundation models in scientific research become more closely integrated with experimental and digital research workflows.
Looking ahead: foundation models and the future of laboratory management
The report positions foundation models in scientific research as a transformative but early-stage technology that will develop alongside traditional computational modeling rather than replace it. For laboratory management, the most immediate priority is preparing organizational systems, governance structures, and workforce capabilities to support emerging hybrid modeling approaches while maintaining scientific reliability, operational consistency, and high-quality research outcomes.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
