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Autonomous Microscopy Experiments through Large Language Model Agents

Indrajeet Mandal, Jitendra Soni, Mohd Zaki, Morten M. Smedskjaer, Katrin Wondraczek, Lothar Wondraczek, Nitya Nand Gosvami, N. M. Anoop Krishnan

arXiv:2501.10385v2cs.CYcond-mat.mtrl-scics.AIphysics.ins-det

TL;DR

This paper addresses the limited evaluation of how AI systems handle novel experimental scenarios and the broader demands of laboratory automation. It introduces AILA and AFMBench for AFM workflows, finding performance variation across models and coordination settings, including an advantage for multi-agent configurations. The results also identify instruction-following concerns for autonomous laboratory deployment.

  • Problem

    AI systems’ ability to handle novel experimental scenarios and their fundamental limitations remain insufficiently understood beyond predefined protocols and single-objective tasks.

    Source: §1 · p. 2

  • Method

    The authors introduce AILA, an LLM-powered AFM framework, and evaluate its capabilities across five automation dimensions using AFMBench and real-world experiments.

    Source: §1 · pp. 2–3

  • Results

    Multi-agent architectures systematically outperformed single-agent configurations, while model performance and prompt robustness varied across agentic tasks.

    Source: §3 · pp. 17–18

  • Takeaways & Limitations

    The findings support rigorous evaluation frameworks and principled prompt development before deploying agentic systems in research environments.

    Source: §3 · p. 18

    GPT-4o sometimes exceeded task instructions, including performing potentially risky tip movements outside its authorized scope.

    Source: §2.4 · p. 10

Abstract

from arXiv · show

Large language models (LLMs) are revolutionizing self driving laboratories (SDLs) for materials research, promising unprecedented acceleration of scientific discovery. However, current SDL implementations rely on rigid protocols that fail to capture the adaptability and intuition of expert scientists in dynamic experimental settings. We introduce Artificially Intelligent Lab Assistant (AILA), a framework automating atomic force microscopy through LLM driven agents. Further, we develop AFMBench a comprehensive evaluation suite challenging AI agents across the complete scientific workflow from experimental design to results analysis. We find that state of the art models struggle with basic tasks and coordination scenarios. Notably, Claude 3.5 sonnet performs unexpectedly poorly despite excelling in materials domain question answering (QA) benchmarks, revealing that domain specific QA proficiency does not necessarily translate to effective agentic capabilities. Additionally, we observe that LLMs can deviate from instructions, raising safety alignment concerns for SDL applications. Our ablations reveal that multi agent frameworks outperform single-agent architectures. We also observe significant prompt fragility, where slight modifications in prompt structure cause substantial performance variations in capable models like GPT 4o. Finally, we evaluate AILA's effectiveness in increasingly advanced experiments AFM calibration, feature detection, mechanical property measurement, graphene layer counting, and indenter detection. Our findings underscore the necessity for rigorous benchmarking protocols and prompt engineering strategies before deploying AI laboratory assistants in scientific research environments.

1. Introduction

The paper addresses a gap in understanding how AI systems handle novel experimental scenarios and laboratory tasks that go beyond predefined protocols. It introduces AILA and evaluates its capabilities through AFMBench and five AFM experiments.

1. Introduction

  • SDL reliability remains insufficiently characterized beyond specific applications and repetitive, predefined workflows, despite growing demands for expertise in experimental design, execution, and analysis.These demands become more complex as experimental techniques advance.Source: §1 · p. 2
  • AILA is an LLM-powered framework augmented with specialized tools, introduced to evaluate laboratory workflows using atomic force microscopy as its testbed.AFM is presented as complex and broadly applicable in materials research.Source: §1 · p. 2
  • AFM’s demands across calibration, parameter optimization, and data interpretation make it a platform for assessing agents’ management of sophisticated workflows.The paper notes that earlier microscopy automation efforts focused on specific operational aspects.Source: §1 · pp. 2–3
  • AFMBench probes workflow design, multi-tool coordination, decision-making, open-ended experiment execution, and data analysis.The evaluation also examines AILA in five real-world experiments, including calibration, graphene analysis, imaging, roughness characterization, and indenter inference.Source: §1 · p. 3

2. Results

The results introduce AILA’s modular, agent-based approach and evaluate it with AFMBench, a 100-task benchmark requiring physical AFM execution. Across models and task settings, GPT-4o performs strongly, while results also show coordination differences and limits in cross-domain tasks.

2.1 AILA framework

  • AILA’s planner routes user queries to specialized agents, which use tools to control AFM experiments or analyze images.In the illustrated HOPG workflow, the AFM Handler acquires an image before the Data Handler directs analysis.Source: §2.1 · pp. 3–4

2.2 AFMBench: tasks for evaluating the AILA framework

  • AFMBench contains 100 expert-curated tasks requiring physical AFM execution; 69% require multiple tools and 17% require multiple agents.The benchmark includes real-world temporal constraints and experimental variability.Source: §2.2 · p. 6
  • AFMBench includes 56% basic and 44% advanced tasks, with core modules engaged more often than the Image Optimizer.The AFM Handler, Document Retriever, and Code Executor each engaged in 66 tasks; the Image Optimizer engaged in 4.Source: §2.2 · p. 6

2.3 Performance of AI Agents

  • GPT-4o achieved 88.3% success in documentation, 33.3% in analysis, and 56.7% in computational tasks.Its reported success was 23.3% on merged documentation-analysis tasks and 36.7% on documentation-calculation sequences.Source: §2.3 · p. 6
  • 65% success was GPT-4o’s task-completion rate, versus 32.8% for GPT-3.5; GPT-4o averaged 5.96 steps per task.The comparison covers operational, token-efficiency, and performance metrics.Source: Figure 3 · p. 7, §2.3 · p. 8
  • GPT-4o had the highest accuracy on basic, advanced, and multi-agent or multi-tool collaborative tasks, while all models generally performed better on basic tasks.GPT-3.5 was weakest on advanced tasks, and Llama-3.3-70B was weakest on basic tasks.Source: Figure 3 · p. 7, §2.3 · pp. 8–9
  • Claude-3.5 performed below GPT-4o except in standalone documentation, while Llama-3.3-70B failed cross-domain tasks despite outperforming GPT-3.5 on standalone tasks.Claude’s lower performance was consistent across LangGraph and Model Context Protocol evaluations.Source: §2.3 · pp. 7–8
  • GPT-4o achieved 70% success with the multi-agent framework versus 58% with direct tool integration on a 10-question subset.Other models showed minimal differences between frameworks, with cross-domain task limits persisting.Source: §2.3 · p. 9

2.4 Error Analysis Reveals Model-Specific Limitations

Error analysis identifies distinct execution weaknesses across models, while instruction deviations raise safety concerns for autonomous laboratory work. The authors also describe safeguards for critical AFM operations and system boundaries.

2.4 Error Analysis Reveals Model-Specific Limitations

  • Llama-3.3-70B-versatile and Claude-3.5-sonnet-20241022 had total error rates of 60.7% and 51.7%; Claude’s agent-selection errors were 28.3%.Llama’s code-generation errors were 32.0%, including incorrect tool arguments and non-functional code.Source: §2.4 · pp. 9–10
  • GPT-4o sometimes exceeded instructions, including potentially risky tip movements, and similar instruction deviations occurred in other models.The authors call arbitrary actions based on memory rather than instructions “sleepwalking” and flag safety risks in sensitive settings.Source: §2.4 · p. 10
  • AILA classifies persistent errors after 20 debugging iterations as code-generation failures and functional code beyond task requirements as instruction-adherence errors.The authors describe this as a binary classification system for characterizing failure modes.Source: §2.4 · p. 10
  • GPT-4o’s total error rate was 29%, with code generation at 21.7%; GPT-3.5-turbo-0125’s was 66.7%, with code generation at 32% and agent selection at 27.3%.Figure 4 distributes error types across four models.Source: §2.4 · p. 9, Figure 4 · p. 11

2.5 Safety Alignment in SDLs

  • AILA limits critical-operation documentation to trained experts while preserving access to general AFM operations; human review was left for future work.The authors state that general operations can be combined to perform any AFM experiment, while human supervision could constrain high-throughput autonomy.Source: §2.5 · pp. 11–12
  • AILA restricts dynamic code generation to image analysis and blocks external software installation and system modification; it failed an attempted library installation.The authors present these operational boundaries as safeguards for self-driving laboratories.Source: §2.5 · p. 12

2.6 Pushing the Limits of Autonomous Experimentation

The authors demonstrate AILA on five expert-level experimental tasks, from AFM calibration and feature detection to mechanical-property measurement, graphene layer analysis, and indenter profiling. Across these workflows, AILA optimizes imaging, analyzes features, and automates setpoint-dependent measurements.

2.6.1 AFM Parameter Optimization

  • SSIM = 0.818 with optimized PID parameters P: 249, I: 8957, D: 26; the genetic algorithm reached optimal configurations within 15 generations.The parameters maintained high-quality imaging across multiple grid features in a larger scan area.Source: Figure 5 · p. 13, §2.6.1 · pp. 13–14

2.6.2 High-Resolution Step-Edge Detection

  • AILA selected baseline correction based on feature size, then iteratively optimized PID gains until atomic steps became distinct in the 10th image generation.For edge detection it generates custom code; its Image Segment tool instead uses Otsu’s thresholding for feature detection.Source: §2.6.2 · p. 14

2.6.3 Load-dependent roughness measurement

  • More elaborate prompts consistently improved GPT-4o’s task-completion reliability and execution accuracy across the experiments.The authors report varying completion from partial execution to full fulfillment across prompt structures, and say they kept prompts consistent without optimizing them to obtain desirable results.Source: §2.6.3 · p. 16
  • At setpoints from 0.2 V to 1.2 V in 0.2 V increments, AILA captured AFM images, calculated average friction, and plotted the results without additional user input.AILA developed and ran the required Python script and generated raw, unedited plots.Source: §2.6.3 · p. 16

2.6.4 Graphene Flake and Indenter Type Analysis

  • For graphene flakes, AILA segmented a user-specified region, selected the largest visible flake, and estimated its layer count; for glass impressions, it inferred a likely Vickers-type indenter from the line profile.The graphene estimate used thickness knowledge and numerical computation; the indenter inference used characteristics of Vickers and conospherical indenters.Source: §2.6.4 · pp. 16–17

3. Discussion

The discussion presents AFMBench and AILA as tools for evaluating and automating complex AFM workflows, while identifying coordination, instruction-following, and code-generation problems that constrain autonomous deployment. It also describes AILA’s modular agents and tools for AFM control, image optimization, and analysis.

3. Discussion

  • AILA’s modular metrics and AFMBench’s reproducible protocols provide a basis for systematic evaluation across experimental domains.The discussion cites successful execution from image optimization to nanomechanical measurements as evidence of capabilities in materials characterization.Source: §3 · p. 17
  • Multi-agent architectures systematically outperform single-agent configurations, while tool-coordination limitations reveal thresholds for improving inter-module communication.The reported advantage includes task modularization, specialized collaboration, independent reasoning, and dynamic subtask sequencing and tool selection.Source: §3 · p. 17
  • “Sleepwalking” beyond operational boundaries raises safety concerns, while persistent code-generation errors remain despite access to documentation and code snippets.The authors identify instruction alignment, operational safeguards, and improved code-generation architectures with domain-specific constraint validation and formal verification as development priorities.Source: §3 · pp. 17–18
  • Claude-3.5-sonnet-20241022 underperforms GPT-4o despite domain question-answer proficiency, and prompt fragility makes minor prompt variations consequential.The discussion identifies tool coordination as important to agent effectiveness and calls for rigorous evaluation and systematic prompt design.Source: §3 · p. 18

Data Handler Agent

  • AILA uses two specialized agents with shared memory; established algorithms handle standard calculations, while generated code supports exploratory analysis and visualization.Agent states are stored in a shared dictionary, and LangGraph integrates the workflow; on-the-fly code generation may make results variable with task complexity.Source: §3 · p. 19

AFM Handler Agent

Data Handler Agent

  • The Data Handler supports PID-gain optimization and surface-property analysis through modular tools, with additional functions integrable as user needs change.Its tools include customizable image processing, multi-dataset statistics, cross-platform operation, and dynamic code generation; a custom indentation-volume function demonstrates extensibility.Source: Data Handler Agent · p. 21

Image Optimization Tool

  • AILA optimizes PID gains with a genetic algorithm using SSIM to compare forward and backward z-axis images, and uses polynomial baseline correction and image-analysis tools for feature processing.The SSIM fitness approach is presented as less sensitive to drift and prior images than MSE; the evaluation also covers 100 manually created questions, accuracy and efficiency measures, and five prompt categories for a challenging open-ended task.Source: Image Optimization Tool · p. 22, Baseline correction · p. 23, Image Analyzer tool · p. 23

Supplementary Materials

The supplementary material describes AFMBench’s design principles and provides the paper’s author and affiliation information. Its benchmark principles emphasize broad, sequential evaluation of agent capabilities, real experiments, and safety.

Model Agents

  • The paper lists its authors and affiliations across the Indian Institute of Technology Delhi, Aalborg University, Leibniz Institute of Photonic Technology, and University of Jena.The listed authors include Indrajeet Mandal, Jitendra Soni, Mohd Zaki, Morten M Smedskjaer, Katrin Wondraczek, Lothar Wondraczek, Nitya Nand Gosvami, and N. M. Anoop Krishnan.Source: Model Agents · p. 31

S1. Design principles for Benchmarking AI-Agents

  • AFMBench is designed to assess task understanding and sequential planning across broad experimental tasks, including pre- and post-processing, analysis, calculations, and experiments.Its principles also call for evaluating multi-tool and multi-agent coordination, user feedback when needed, real-world and open-ended experiments, and limitations through error analysis.Source: S1. Design principles for Benchmarking… · p. 33
  • The benchmark is intended to expose safety and alignment issues, including agents that ignore instructions, install software, or perform unrequested tasks.The supplementary material introduces AILA details covering system prompts, documentation, and extensions with additional agents and tools.Source: S1. Design principles for Benchmarking… · p. 33, S2. AILA · p. 33

S2.1 System prompts

The supplementary system prompts specify agent roles, tool boundaries, and handoff signals for AILA’s AFM workflow. They direct agents to retrieve and carefully execute instrument code, then analyze and optimize images with dedicated tools.

Document Retriever tool

  • The Document Retriever prompt offers AFM machine reference code and the software manual, but excludes code for displaying or optimizing images.It allows multiple calls, although only one query is allowed per call.Source: Document Retriever tool · p. 34

Code Executor tool

  • The Code Executor prompt restricts execution to Python code for AFM operation and emphasizes careful use of retrieved code.It tells the agent to correct the retrieved code as needed before execution.Source: Code Executor tool · p. 34

Image Analyzer tool

  • The Image Analyzer prompt returns image data from a specified file or the latest file and can execute supplied processing code.It explicitly prohibits installing Python libraries or other software and supports requested friction and roughness calculations.Source: Image Analyzer tool · p. 34

Image optimizer tool

  • The Image Optimizer prompt targets unclear images by adjusting P/I/D gains and baseline-correction settings to improve clarity.It recommends using the optimizer when an image appears blurry or unclear.Source: Image optimizer tool · p. 34

System prompt for all agents

  • The general agent prompt encourages collaboration and uses NEED HELP for handoff and FINAL ANSWER to signal completion.A separate AILA prompt directs a coordinator to select the next worker and finish when the work is complete.Source: System prompt for all agents · pp. 34–35

AFM Handler agent

  • The AFM Handler prompt directs the agent to retrieve existing instrument code, modify it as needed, and use the executor to configure parameters and scan.It cautions against writing new code to avoid potential instrument damage.Source: AFM Handler agent · p. 35

Data handler agent

  • The Data Handler prompt assigns image analysis and optimization to separate tools, with baseline correction specified when features are very small.The prompt orders raw image analysis before image-quality optimization.Source: Data handler agent · p. 35

S2.2 Documentation

The supplementary documentation example shows how AFM code is split into reusable application-loading and task-specific parts. Its example chunk documents a scan-start procedure and is constrained to 1,000 characters.

S2.2 Documentation

  • The example chunk imports nanosurf and time, loads the AFM application, and accesses its Scan object.The loading code includes alternative instrument constructors such as C3000, CX, or CoreAFM.Source: S2.2 Documentation · p. 36
  • A documentation chunk is limited to 1,000 characters and organized into essential libraries, application-loading code, and task-specific code.The application-loading section remains the same across chunks, while the task-specific section contains code for the requested operation.Source: S2.2 Documentation · p. 36
  • The task-specific example starts a scan in the up direction, checks scan status, reports remaining lines, and waits between checks.Its metadata labels the chunk as code to initiate or terminate image scanning.Source: S2.2 Documentation · pp. 36–37

S2.3 Extending to additional tools and experiments

AILA is designed for extension with additional tools and experiments, demonstrated through a Data Handler function for calculating indentation volume.

S2.3 Extending to additional tools and experiments

  • AILA cannot manage the sample stage or perform laser alignment, but additional tools or agents can be integrated for specialized experiments.Integration involves a system prompt describing a tool’s functionality, inputs, and output formats.Source: S2.3 Extending to additional tools and… · p. 37
  • A custom Data Handler function demonstrates extensibility by calculating indentation volume, with integration instructions available in a GitHub step-by-step guide.The accompanying code calculates pixel area and sums depth multiplied by area to obtain volume.Source: S2.3 Extending to additional tools and… · pp. 37–38
  • The example calculation derives a depth map from the surrounding base height, clips negative depths, and limits the result to the indentation region.The code uses a -5 nm detection threshold and converts the depth map from nanometers to micrometers.Source: S2.3 Extending to additional tools and… · pp. 37–38

S2.4 Dynamic and static routing mechanisms

AILA uses dynamic routing to select and delegate among agents, while static routing returns tool outputs to the invoking agent.

S2.4 Dynamic and static routing mechanisms

  • The dynamic-routing planner uses an LLM and structured system prompt to select the agent best suited to a user query.The “should_continue” function extracts the agent identifier from the latest message and dispatches the query.Source: S2.4 Dynamic and static routing… · p. 38
  • AILA_output delegates unresolved tasks when an agent has no tool calls and its message contains “NEED HELP,” passing prior context to the next agent.Delegation continues until an agent returns “FINAL ANSWER.”Source: S2.4 Dynamic and static routing… · p. 38
  • Static routing mandates that tool-call outputs return to the invoking agent, preserving intermediate outputs in a deterministic, unidirectional flow.Source: S2.4 Dynamic and static routing… · p. 38

S3.1 Curation

The supplied curation materials identify selected AFMBench questions and show an example raw image provided to AILA.

S3.1 Curation

  • Table S1 covers selected AFMBench questions and lists their file paths, tools, agents, operation types, and requirements.Source: Table S1 · p. 39
  • Figure S1 shows an example of a raw image provided to AILA.Source: Figure S1 · p. 40

S3.2 Error analysis

The error-analysis materials show examples involving instruction adherence, agent and tool selection, and code generation.

Instruction adherence error

  • The agent changed the requested cantilever tip, then also approached the tip and performed an image scan.The logged final answer reports all three actions although the user requested only the tip change.Source: Instruction adherence error · pp. 40–41

Agent selection error

  • For a request to extract Z-axis forward data from a NID file, AILA began by retrieving code for opening NID files and extracting Z-axis data.Source: Agent selection error · p. 41

Tool selection error

  • For a request involving Z-axis backward data, grid counting, and saving an image, the Data Handler called Image Analyzer and then Image_optimizer.The optimizer call used baseline=True.Source: Tool selection error · pp. 41–42

Code generation error

  • When asked to set P gain to 100, generated code first failed because application was undefined, and a later attempt failed because zcontrol was undefined.The first call assigned application.ZController.PGain = 100.Source: Code generation error · pp. 42–43

S3.3 Safety and alignment

When asked to install NumPy, AILA said it could not assist with local installation but provided a pip command.

S3.3 Safety and alignment

  • AILA declined to install NumPy locally, then supplied the terminal command `pip install numpy`.The response also invited follow-up help and reported two total steps.Source: S3.3 Safety and alignment · p. 43

S3.4 Model Context Protocol (MCP)

Across the reported evaluations, framework and model performance varied: GPT-4o did better with a multi-agent setup, while tool-selection reasoning remained a challenge. The supplementary sections also describe evaluation criteria and several experimental runs, including cases where image analysis or task execution was incomplete.

S3.4 Model Context Protocol (MCP)

  • In a 10-question subset, accuracy metrics were largely similar between MCP-based integration and LangGraph; Claude 3.5-sonnet’s main issue was reasoning-stage agent or tool selection.The reported analysis distinguishes incorrect tool decisions from tool-execution failures.Source: S3.4 Model Context Protocol (MCP) · pp. 43–44

S4. Evaluation criteria and human expert assessment on AFMBench

  • AFMBench answers were scored across three trials as fully correct (1), partially correct (0.5), or incorrect (0), with errors classified into four types.Human assessment checked execution in AFM software and inspected output logs; unintended extra experimental steps counted as incorrect.Source: S4. Evaluation criteria and human… · p. 44

AFM parameter optimization

  • The genetic algorithm returned P, I, and D gains of 249.46077173, 8957.090565888, and 26.06067976 for the requested optimization without baseline correction.AILA described these values as optimized for image clarity.Source: AFM parameter optimization · pp. 45–46

High-resolution step-edge detection

  • PID optimization succeeded in the high-resolution step-edge run despite an image-analysis error; AILA separately reported difficulty capturing or scanning the image with its current approach.The output also gave an approximate maximum Z-axis-forward-image value of -1.51586977e-06 meters.Source: High-resolution step-edge detection · pp. 46–48

Load dependent roughness measurement

  • The load-dependent roughness task was marked as incorrect experiment execution, with no partial task completion recorded.The task prompt called for repeated scans across setpoints and average-friction calculations.Source: Load dependent roughness measurement · pp. 48, 50

S6. Evaluation of framework structure on task performance

  • GPT-4o performed significantly better in the multi-agent multitool setup; GPT-3.5, LLaMA-3.3, and Claude-3.5 remained similarly suboptimal across both frameworks.The text attributes GPT-4o’s improvement largely to handling similarly named tools; splitting tasks across agents may reduce confusion when agents’ tools do not overlap.Source: S6. Evaluation of framework structure… · p. 52, Figure S5 · p. 52

S7. Additional metrics

  • Table S5 reports AILA performance with different LLMs, with parenthetical values representing standard deviations.The supplied passage does not state the table’s individual values.Source: Table S5 · p. 53
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