Evaluating Large Language Models in Scientific Discovery
Unlocking Scientific Breakthroughs: A New Benchmark for AI-Driven Discovery
Our latest analysis introduces the Scientific Discovery Evaluation (SDE) framework, a scenario-grounded benchmark revealing how Large Language Models perform in real-world biological, chemical, material, and physical research. We expose critical gaps and chart a path for developing AI that truly accelerates scientific progress.
Executive Impact at a Glance
The SDE framework provides a comprehensive, multi-layered assessment of LLMs, highlighting their current capabilities and the strategic areas for future development to unlock AI's full potential in scientific discovery.
Discovery Questions Evaluated
Scientific Domains Covered
Research Scenarios Assessed
Project-level Workflows Simulated
Unique Hard Problems Solved
Deep Analysis & Enterprise Applications
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Biology
Chemistry
Materials
Physics
LLM Performance in Biology
In biology, top-tier LLMs on the SDE benchmark achieve scores around 0.71 (Claude-4.1-opus). This contrasts with general Q&A benchmarks where scores are significantly higher, indicating a clear gap in handling context-dependent biological discovery tasks. Projects like protein design show promise, but fine-grained understanding of specific biological processes remains a challenge.
LLM Performance in Chemistry
Chemistry-related SDE tasks reveal LLM performance around 0.60 (Claude-4.5-sonnet). While models like GPT-5 excel in retrosynthesis planning (0.85), they struggle with NMR structure elucidation (0.23). This variability underscores the need for targeted improvements in specific chemical reasoning abilities rather than broad knowledge.
LLM Performance in Materials Science
Materials science tasks on SDE show top LLM scores around 0.75 (GPT-5). While LLMs can generate novel crystal structures and assist in TMC optimization, performance on nuanced property predictions (e.g., oxidation states, spin states) remains lower. Project-level success, however, can still be high due to serendipitous exploration and optimization directions.
LLM Performance in Physics
For physics-related SDE questions, LLMs reach an average score of 0.60 (GPT-5). Symbolic regression tasks demonstrate LLMs' ability to iteratively discover governing equations, showcasing their capacity for structured exploration. However, challenges persist in areas like quantum information and condensed matter theory, indicating limits in handling complex theoretical frameworks.
LLM Performance: General Q&A vs. Scientific Discovery
Existing benchmarks (GPQA, MMMU) test decontextualized knowledge, while SDE measures scenario-grounded scientific discovery. Top-tier LLMs score lower on SDE questions compared to general Q&A, highlighting that static knowledge doesn't equate to discovery readiness.
| Benchmark Type | Key Characteristics | Representative LLM Scores |
|---|---|---|
| General Q&A Benchmarks |
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| SDE Scientific Discovery |
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Performance Plateaus: Scaling & Reasoning
Marginal Gains From increased model size and reasoning in discovery tasksWhile reasoning improves accuracy, overall performance on SDE starts to saturate for top models (GPT-5 series) even with increased reasoning effort. Scaling up model size also shows marginal gains (GPT-5 over 03). This implies that current strategies (more compute, bigger models) are less effective for scientific discovery's unique demands like problem formulation and hypothesis refinement.
Identifying Common LLM Weaknesses in Scientific Discovery
Challenge: Top-performing LLMs (GPT-5, Grok-4, DeepSeek-R1, Claude-Sonnet-4.5) show highly correlated accuracy profiles, frequently failing on the same difficult scientific discovery scenarios, such as complex MOF synthesis questions. A dedicated "SDE-hard" subset of 86 questions reveals all LLMs scoring less than 0.12, indicating shared systematic weaknesses likely inherited from similar pre-training data distributions.
Impact: This convergence of errors limits the effectiveness of simple ensemble strategies and points to fundamental gaps in their understanding or reasoning for truly challenging scientific tasks.
Observation: While all models struggle, GPT-5-pro demonstrates a competitive advantage, correctly answering 9 questions (10.5% of SDE-hard) where all other models failed, despite its higher computational cost.
Conclusion: To overcome these shared failure modes, there's a critical need for diversifying pre-training data sources and exploring novel inductive biases beyond current paradigms.
The Iterative Scientific Discovery Loop
SDE evaluates LLMs within an iterative discovery loop, mimicking the authentic process of scientific research. LLMs act as hypothesis generators, executing analyses, and interpreting outcomes to refine their understanding and accelerate discovery.
Hypothesis Generation
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Experiment/Simulation Design
→
Observation Interpretation
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Hypothesis Refinement
Projects with well-structured data (protein design, TMC optimization, symbolic regression) show significant gains. Interestingly, high scenario-level proficiency doesn't always guarantee project success (e.g., retrosynthesis struggled despite high Q&A scores), while low scenario scores (e.g., TMC optimization) could still yield excellent project-level efficiency due to "serendipitous exploration."
No Single LLM Dominates Scientific Discovery
No Dominant LLM Leadership rotates across scientific discovery projectsAcross the eight diverse scientific discovery projects evaluated, no single LLM consistently outperforms others; leadership frequently rotates depending on the task. This variability underscores that current LLMs are far from achieving "true scientific superintelligence" and highlights the composite nature of scientific discovery, which requires balanced proficiency across many interdependent research scenarios.
Projected ROI for AI in Scientific Discovery
Estimate the potential time savings and cost efficiencies your organization could achieve by integrating advanced LLMs into your scientific research workflows.
Annual Cost Savings
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Annual Hours Reclaimed
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Strategic Roadmap for AI in Scientific Discovery
Our findings delineate key areas for strategic investment and development to transform LLMs into truly collaborative scientific partners. These phases prioritize actionable steps towards building more capable and discovery-ready AI.
01. Targeted Training & Problem Formulation
Shift focus from indiscriminate scaling to specific training on problem formulation, hypothesis generation, and iterative reasoning. This involves leveraging domain-specific datasets and curricula designed for scientific methodology rather than general knowledge.
02. Data Diversification & Inductive Biases
Address shared failure modes by diversifying pre-training data sources and exploring novel inductive biases. This will mitigate common systematic weaknesses across frontier models, leading to more robust and versatile scientific AI.
03. Robust Tool Integration & Executable Actions
Enhance actionable intelligence by tightly coupling LLMs with domain-specific simulators, structure builders, and computational libraries. Prioritize executable actions, robust debugging mechanisms, and iterative refinement based on real-world experimental feedback.
04. Tailored Reinforcement Learning for Science
Develop reinforcement learning strategies specifically optimized for the iterative, often uncertain nature of scientific reasoning. This approach will differ from methods tailored for coding or mathematics, driving advancements unique to scientific discovery.
Ready to Pioneer the Future of Scientific AI?
The SDE framework provides clear guidance for advancing LLMs in scientific discovery. Partner with us to strategically integrate these insights into your R&D, overcoming current limitations and accelerating your path to breakthrough innovations.
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