- The paper presents a roadmap for integrating AI and MPS, highlighting a bidirectional relationship that accelerates both scientific discovery and AI innovation.
- It employs a rigorous taxonomy of AI techniques—such as LLMs, reinforcement learning, and physics-informed networks—to enhance reproducibility and interpretability in scientific workflows.
- The study advocates for interdisciplinary collaboration, scalable infrastructure, and targeted workforce development to support domain-specific foundation models and practical AI applications.
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS): An Expert Analysis
Strategic Vision and Bidirectional Impact
This community white paper, originating from the NSF Future of AI+MPS Workshop, provides a comprehensive roadmap for the integration of AI with the mathematical and physical sciences (MPS), encompassing astronomy, chemistry, materials research, mathematics, and physics. The central thesis is the recognition of a bidirectional relationship: AI is not only a transformative tool for scientific discovery, but MPS domains are uniquely positioned to advance the science of AI itself. The document advocates for a strategic approach that enables research in both directions, builds an interdisciplinary AI+MPS community, and fosters education and workforce development.
The paper emphasizes that MPS domains have historically driven foundational advances in AI, as evidenced by the 2024 Nobel Prizes in Physics and Chemistry, and that the current moment is critical for leveraging AI to accelerate scientific discovery while simultaneously using scientific principles to enhance AI robustness, interpretability, and efficiency. The strategic vision is operationalized through recommendations for funding agencies, educational institutions, and individual researchers, with a focus on interdisciplinary collaboration, scalable infrastructure, and the cultivation of an AI-literate workforce.
Taxonomy and Technical Landscape of AI in MPS
The report provides a rigorous taxonomy of AI methodologies relevant to MPS, distinguishing between shallow and deep ML, supervised/unsupervised/self-supervised paradigms, discriminative/generative models, deterministic/stochastic frameworks, reasoning/agentic systems, and the emergence of foundation models. It highlights the increasing importance of LLMs, reinforcement learning (RL), and symbolic AI/formal methods in scientific workflows.
A key technical insight is the role of generative models (GANs, VAEs, normalizing flows, diffusion models) in scientific inference, anomaly detection, and surrogate modeling. The document also discusses the integration of physical laws into AI architectures (e.g., Hamiltonian/Lagrangian neural networks, physics-informed neural networks), the challenges of uncertainty quantification (UQ), and the need for robust, reproducible, and interpretable AI systems. The analysis underscores the necessity of domain-specific foundation models and the potential of agentic AI systems to serve as co-pilots or autonomous scientific collaborators.
Cross-Disciplinary Opportunities and Infrastructure
The paper identifies several cross-cutting opportunities for advancing AI+MPS:
- Diverse Funding Streams: Advocacy for institute-scale, project-scale, and individual investigator funding, with mechanisms to support polymathic researchers and industry collaborations.
- Science of AI: MPS domains are positioned to lead research into the fundamental principles of AI, including the development of new architectures, efficient algorithms, and physics-based generative models.
- Scalable Infrastructure: Emphasis on the need for centralized computing resources, data management, curated benchmarks, and sustained software/model maintenance. The report calls for national-scale facilities and long-term support for data and model repositories.
- Interdisciplinary Collaboration: Mechanisms for knowledge transfer, workshops, hackathons, and joint working groups to bridge disciplinary silos and accelerate innovation.
- Key AI Techniques: Detailed discussion of simulation-based inference (SBI), multi-scale simulations, UQ, foundation models, experimental control via RL, and data-efficient methods, with domain-specific applications and open research challenges.
Domain-Specific Analyses
Astronomy (AST)
AI has enabled real-time classification, anomaly detection, simulation acceleration, and multi-modal data integration in astronomy. The field provides open, physics-rich datasets and a low-risk environment for AI experimentation. Opportunities include extending SBI to heterogeneous data, developing multi-scale emulators, rigorous UQ, and operational deployment of RL for observatory control.
Chemistry (CHE)
AI has transformed computational chemistry, catalysis, materials synthesis, and protein design. Chemistry has influenced AI through the development of diffusion models and physics-informed architectures. Key challenges include generalization beyond training data, adaptive experimental control, and the integration of mechanistic priors into generative models.
Materials Research (DMR)
Materials research operates in the small-data regime, requiring robust, uncertainty-aware models and multi-scale simulation techniques. AI has accelerated molecular discovery, automated synthesis, and enabled self-driving labs. DMR drives the development of symmetry-aware networks, multi-modal generative models, and domain-informed benchmarks.
Mathematical Sciences (DMS)
Mathematics and statistics underpin the theoretical foundations of AI, from optimization and training dynamics to generative modeling and functional analysis. DMS researchers are advancing AI through the development of scalable algorithms, formal reasoning agents, and new frameworks for abstraction and compositionality. Opportunities include AI-assisted theorem proving, scalable statistical inference, and the integration of topological data analysis (TDA) into neural architectures.
Physics (PHY)
AI is used for real-time experimental control, anomaly detection, fast simulation, and hybrid modeling in physics. Physics has contributed to AI through the development of equivariant networks, neural scaling laws, and mechanistic interpretability. Research priorities include scaling proofs of concept, rigorous UQ, hybrid quantum/classical AI, and the establishment of a "Physics of AI" research thrust.
Education, Workforce, and Scientific Integrity
The report provides a granular analysis of educational needs across career stages, advocating for modular, stackable AI literacy frameworks, interdisciplinary degree programs, and industry partnerships. It addresses the challenges of integrating AI into curricula, maintaining scientific rigor, and ensuring broad access to computational resources. The document also engages with questions of scientific integrity, data provenance, interpretability, and public engagement, emphasizing the need for ethical standards and transparent evaluation frameworks.
Implications and Future Directions
The integration of AI with MPS domains is poised to accelerate scientific discovery, enhance the robustness and interpretability of AI systems, and establish new interdisciplinary fields. The report identifies several strong claims and open challenges:
- AI+MPS as a Two-Way Street: MPS domains are not merely consumers of AI technology but active contributors to its advancement.
- Need for Domain-Informed Foundation Models: Generic foundation models are insufficient; domain-specific pretraining and fine-tuning are essential for scientific applications.
- Scientific Rigor and Interpretability: The adoption of AI in MPS must be accompanied by rigorous UQ, interpretability, and reproducibility standards.
- Scaling and Resource Requirements: The computational and data demands of AI-enabled science necessitate national-scale infrastructure and sustained investment.
- Education and Workforce Development: The unprecedented scope of upskilling required demands modular, accessible educational pathways and support for interdisciplinary researchers.
Future developments will likely include the emergence of autonomous AI scientists, the formalization of the "Science of AI" as a research domain, and the integration of AI into the design and operation of next-generation scientific experiments. The report calls for intentional strategies to position the MPS community as a leader in the coming waves of AI, with the potential for transformative breakthroughs in both science and AI.
Conclusion
This white paper provides an authoritative, technically rigorous roadmap for the future of AI+MPS, grounded in the recognition of mutual innovation and the necessity of interdisciplinary collaboration. The recommendations for funding, infrastructure, education, and research priorities are actionable and well-justified. The implications for both practical scientific workflows and the theoretical foundations of AI are profound, and the document sets a clear agenda for the next decade of AI+MPS research and development.
