Human-AI collaborative autonomous synthesis with pulsed laser deposition for remote epitaxy

TL;DR

This paper presents a human-AI collaborative workflow that integrates large language models with autonomous pulsed laser deposition experiments to accelerate remote epitaxy research and optimize growth conditions.

Contribution

It introduces a novel human-AI collaborative system that enhances autonomous experimental workflows with hypothesis generation and analysis capabilities.

Findings

Accelerated hypothesis formation and experimental design.

Identified a low-O₂ pressure, low-temperature synthesis window.

Demonstrated the necessity of a two-step Ar/O₂ deposition process.

Abstract

Autonomous laboratories typically rely on data-driven decision-making, occasionally with human-in-the-loop oversight to inject domain expertise. Fully leveraging AI agents, however, requires tightly coupled, collaborative workflows spanning hypothesis generation, experimental planning, execution, and interpretation. To address this, we develop and deploy a human-AI collaborative (HAIC) workflow that integrates large language models for hypothesis generation and analysis, with collaborative policy updates driving autonomous pulsed laser deposition (PLD) experiments for remote epitaxy of BaTiO$_3$/graphene. HAIC accelerated the hypothesis formation and experimental design and efficiently mapped the growth space to graphene-damage. In situ Raman spectroscopy reveals that chemistry drives degradation while the highest energy plume components seed defects, identifying a low-O$_2$ pressure low-temperature synthesis window that preserves graphene but is incompatible with optimal BaTiO$_3$ growth. Thus, we show a two-step Ar/O$_2$ deposition is required to exfoliate ferroelectric BaTiO$_3$ while maintaining a monolayer graphene interlayer. HAIC stages human insight with AI reasoning between autonomous batches to drive rapid scientific progress, providing an evolution to many existing human-in-the-loop autonomous workflows.