Scientific Foundation Models

Updated 28 November 2025

Scientific Foundation Models are large-scale, multi-modal neural architectures pre-trained on diverse scientific data to support flexible in-context and few-shot learning.
They integrate modalities such as text, images, code, and measurements, leveraging transformer backbones, graph networks, and physics-informed losses for robust cross-domain performance.
Their training minimizes composite, self-supervised losses over heterogeneous datasets, achieving strong zero-shot generalization and efficient domain adaptation.

Scientific foundation models (SFMs) are large-scale neural architectures pre-trained on heterogeneous, domain-specific data spanning text, code, measurements, images, simulations, and other structured modalities. They deliver a flexible, general-purpose backbone for scientific tasks via in-context prompting, adaptation, or minimal fine-tuning, frequently yielding strong zero- or few-shot transfer and enabling both integrative analysis and automation across diverse branches of the physical, life, and environmental sciences.

1. Core Principles and Formal Characterization

SFMs extend the foundation model paradigm—ubiquitous in NLP and vision—into domains governed by experimental, computational, or physical principles. A scientific foundation model is formally expressed as a parametric family

$\mathcal H = \{ f_\theta : \mathcal X \to \mathcal Y \mid \theta \in \mathbb R^p \}$

where $\mathcal X$ and $\mathcal Y$ may be multimodal (sequences, graphs, images, fields). Training proceeds by minimizing a surrogate empirical risk over a massive, heterogeneous scientific corpus $\mathcal D$ : $\theta^* = \arg \min_\theta \sum_{x \in \mathcal D} \ell \big( f_\theta(x), \tau(x) \big)$ with $\tau(x)$ a self-supervision target (e.g., next-token, masked patch, contrastive pairing) (Liu et al., 17 Oct 2025, Fu et al., 15 Oct 2024).

Key invariants across scientific settings include:

Scale and universality: Pre-training with $|\theta| \gtrsim 10^8$ – $10^{13}$ parameters spanning multiple disciplines (Liu et al., 17 Oct 2025).
Emergent generality: In-context (zero-/few-shot) transfer and cross-task adaptation are enabled by high-capacity, modality-agnostic architectures.
Physical or structural inductive biases: Domain-specific symmetries (e.g., invariance to coordinate transformations), conservation constraints, or operator structure.
Multi-modality: SFMs integrate and align representations across images, spectra, equations, code, sensor logs, and natural language.

2. Architectural Innovations and Training Objectives

The transformer (self-attention) backbone is the dominant motif, often merged with GNNs or physics-informed operator layers. Typical modality-specific enhancements include:

Text/language/corpus: Encoder-decoder or decoder-only transformers, pre-trained with token-level cross-entropy (Hatakeyama-Sato et al., 14 Jun 2025), masked language modeling (Wadell et al., 20 Oct 2025), or contrastive learning (Zhang et al., 2023).
Vision/Science Images: Vision transformer (ViT)-based encoders or convolutional hybrids, pre-trained using masked autoencoding or InfoNCE losses (Zhang et al., 2023).
Numerical fields/physics: Fourier Neural Operators for mesh-based PDEs (Subramanian et al., 2023, Totounferoush et al., 24 Mar 2025), or GNN/E(3)-equivariant architectures for molecular/atomistic data (Yuan et al., 13 Mar 2025).
Multimodal alignment: Joint text-image (CLIP, InfoNCE), text-spectrum, or tabular fusion via contrastive or cross-modal attention objectives (Zhang et al., 2023, Hatakeyama-Sato et al., 14 Jun 2025).

2.2 Self-supervised and Physics-guided Losses

Pretraining optimizes composite losses:

Language/text: Next-token cross-entropy, masked modeling.
Vision: Masked patch prediction, image-text contrastive loss.
Operator learning: Mean squared error for field approximations, force matching ( $L_F = \sum_{m,i} \|F_{m,i}^\mathrm{pred} - F_{m,i}^\mathrm{ref}\|^2$ ), or constraint (PDE residual) objectives ( $L_\mathrm{res} = \mathbb E_j \| \mathcal{G}(\hat u_j; \lambda_j)-f_j \|^2$ ) (Yuan et al., 13 Mar 2025, Totounferoush et al., 24 Mar 2025).
Physical regularization: Auxiliary losses enforcing mass/energy conservation, symmetry, boundary conditions (Yu et al., 5 Apr 2025).

3. Adaptation, Transfer, and Generalization Mechanisms

3.1 Prompting, Fine-tuning, and In-Context Learning

Prompt engineering: Use of scientific role/system instructions and dynamic context to guide generation or analysis (Hatakeyama-Sato et al., 14 Jun 2025).
Domain adaptation: Re-minimization of pretraining loss on in-domain corpora or retraining lightweight adapters (Yu et al., 5 Apr 2025).
Few-shot learning: Models are applied to new tasks by providing small, labeled context sequences, often without further weight updates (in-context learning) (Berghaus et al., 14 Oct 2025).
Zero-shot transfer: Models generalize to new data distributions, shifted physics, or even new operators (Subramanian et al., 2023, Totounferoush et al., 24 Mar 2025).

3.2 Scaling Laws and Compute-Optimal Training

Empirical error curves exhibit power-law decay in both model and data size ( $\mathrm{Error} \sim N^{-\alpha} D^{-\beta}$ ), but scientific domains deviate from NLP's data–parameter balance due to data manifold structure and concept exposure (Wadell et al., 20 Oct 2025). Bayesian penalized scaling law fitting guides the discovery of compute-optimal regimes.

4. Application Domains and System Integration

Domain	Key SFM Architectures	Representative Tasks
Chemistry & Materials	MPNN, GNN, FNO, transformer (e.g., MIST)	MLIPs, molecular property prediction, atomistic MD, generative
Laboratory Automation	Multimodal transformer, LLM, vision-action	Protocol generation, robotic control, experimental agents
Environmental Science	Spatiotemporal transformer, multimodal GNN	Forecasting, monitoring, assimilation, downscaling, decision
Biomedical Imaging	ViT + language, domain-aligned CLIP	Radiology retrieval, histopathology classification, VQA
Literature and Knowledge	LLM, retrieval-augmented transformer	Literature retrieval, multi-doc QA, knowledge-graph reasoning

In all cases, multimodal alignment and domain-adaptive pretraining are critical for bridging the gap between disparate data types and scientific reasoning requirements.

5. Evaluation Methodologies and Benchmarking

Benchmarks for SFMs have been constructed to assess literature question answering (SciArena (Zhao et al., 1 Jul 2025)), multimodal-multidocument integration (M3SciQA (Li et al., 6 Nov 2024)), operator generalization (OC20, MD17, PDE transfer (Yuan et al., 13 Mar 2025, Subramanian et al., 2023)), and environmental prediction (ClimaX, Aurora, SSL4EO (Yu et al., 5 Mar 2025, Yu et al., 5 Apr 2025)).

Key metrics include:

Task success rate $S$ and zero-shot accuracy $Acc_\mathrm{zs}$ in robotics (Hatakeyama-Sato et al., 14 Jun 2025).
Retrieval effectiveness (MRR, recall@ $k$ ), QA accuracy, and BERTScore for literature and benchmark QA (Li et al., 6 Nov 2024, Zhao et al., 1 Jul 2025).
Regression/classification losses (RMSE, MAE, AUROC) for property and forecast prediction (Wadell et al., 20 Oct 2025, Yuan et al., 13 Mar 2025).
Physics consistency error, simulated environment robustness, and domain shift sensitivity (Totounferoush et al., 24 Mar 2025, Yu et al., 5 Apr 2025).
End-to-end automation benchmarks involve composite scores (e.g., $w_1$ (novelty) $+w_2$ (yield) $+w_3$ (coherence)) (Hatakeyama-Sato et al., 14 Jun 2025).

Despite progress, SFMs underperform human experts in high-complexity, multimodal tasks (e.g., M3SciQA: GPT-4o MRR 0.5 versus human 0.796) (Li et al., 6 Nov 2024).

6. Key Challenges and Open Problems

Multimodal data fusion: Scarcity of paired scientific image/text/spectrum datasets limits robust grounding and cross-modal transfer (Hatakeyama-Sato et al., 14 Jun 2025, Zhang et al., 2023).
Physical constraint integration: Embedding conservation laws, boundary conditions, or symmetry via hybrid or loss-based methods remains domain- and scale-dependent (Yu et al., 5 Apr 2025, Totounferoush et al., 24 Mar 2025).
Operational safety and reliability: Especially acute in automated labs—human-in-the-loop supervision, virtual sandboxes, and standardized simulation interfaces are critical (Hatakeyama-Sato et al., 14 Jun 2025).
Explainability and uncertainty: Most SFMs are "black boxes." Efforts to integrate physical attribution, uncertainty quantification, and mechanistic interpretability are nascent (Wadell et al., 20 Oct 2025, Yu et al., 5 Apr 2025).
Rare event and out-of-distribution generalization: Compounded by imbalanced data (e.g., environmental extremes) and distribution shift; necessitates active learning and continual updating (Yu et al., 5 Apr 2025).

7. Roadmap and Future Directions

Embodied and autonomous agents: Integration of SFMs with real-world robotics, digital twins, and agent-based orchestration to move towards closed-loop, autonomous scientific discovery (Liu et al., 17 Oct 2025, Hatakeyama-Sato et al., 14 Jun 2025).
Physics-guided and hybrid architectures: Deepening the fusion between mechanistic models and data-driven representations—e.g., physics-informed attention, not only as regularizers but baked into the architecture (Yu et al., 5 Apr 2025).
Efficient adaptation and carbon minimization: Parameter-efficient fine-tuning (PEFT), quantization, scenario-based continual learning for greener science (Zhu et al., 7 May 2024).
Ethics, fairness, and reproducibility: Open data/model repositories, transparent reasoning logs, and clear provenance to mitigate bias, hallucination, and opacity (Liu et al., 17 Oct 2025, Fu et al., 15 Oct 2024).
Expanded, standardized evaluation suites: Comprehensive, cross-modal benchmarking for scientific performance, security, and knowledge integration, tailored by domain (Zhao et al., 1 Jul 2025, Li et al., 6 Nov 2024, Zhu et al., 7 May 2024).