Christopher M. Rouleau

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cond-mat.mtrl-sci2026

Active Learning for Tractable and Reproducible Pulsed Laser Deposition

Jackson S. Bentley, Christopher Rouleau, Ilia N. Ivanov +8

This paper shows how data-driven machine learning approaches can improve growth control, reproducibility, and physical insight in the pulsed laser deposition (PLD) growth of correl…

cond-mat.mtrl-sci2025

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

Asraful Haque, Daniel T. Yimam, Jawad Chowdhury +10

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…

cond-mat.mtrl-sci2025

Deep learning with reflection high-energy electron diffraction images to predict cation ratio in Sr $_{2 x}$ Ti $_{2 (1 - x)}$ O $_{3}$ thin films

Sumner B. Harris, Patrick T. Gemperline, Christopher M. Rouleau +2

Machine learning (ML) with in situ diagnostics offers a transformative approach to accelerate, understand, and control thin film synthesis by uncovering relationships between synth…

Active Learning for Tractable and Reproducible Pulsed Laser Deposition

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

Deep learning with reflection high-energy electron diffraction images to predict cation ratio in Sr2x​Ti2(1−x)​O3​ thin films

Deep learning with reflection high-energy electron diffraction images to predict cation ratio in Sr $_{2 x}$ Ti $_{2 (1 - x)}$ O $_{3}$ thin films