Integrating Domain-Specialized Language Models with AI Measurement Tools for Deterministic Atomic-Resolution Experimentation

TL;DR

This paper presents a domain-specific language model framework for autonomous control of scanning probe microscopy, enabling real-time atomic-resolution experiments with high accuracy and deterministic execution on consumer hardware.

Contribution

It introduces a specialized, modular architecture that combines small language models with AI measurement tools for deterministic scientific experimentation.

Findings

Achieved real-time atomic-resolution SPM experiments at room temperature.

Reduced perplexity from 1.44 to 1.20, improving model reliability.

Model reached 99.3% and 95.2% command accuracy, outperforming OpenAI o4-mini.

Abstract

Self-driving laboratories based on large language models promise to transform scientific discovery through general experimental automation. However, realizing this vision on precision platforms remains challenging, requiring deterministic execution and effective domain adaptation under strict physical constraints. We address these requirements through a framework that specializes in small language models for autonomous control of scanning probe microscopy, coordinating task-specific models with AI-driven measurement tools. We demonstrate real-time, atomic-resolution SPM experiments at room temperature, achieving instruction-level control and multi-step experimental planning. Fine-tuning reduces perplexity from 1.44 to 1.20 and improves reliability, with the adapted model reaching 99.3% and 95.2% command accuracy, outperforming OpenAI o4-mini on domain-specific tasks. This architecture achieves lower computational cost while maintaining deterministic execution and enabling deployment on consumer-grade hardware. This work bridges probabilistic language models with deterministic experimental control through a modular, domain-specialized architecture, providing a generalizable pathway toward scalable and trustworthy self-driving laboratories across diverse scientific platforms.