AIMD-L: An automated laboratory for high-throughput characterization of structural materials for extreme environments

TL;DR

AIMD-L is an automated laboratory that enables rapid, high-throughput characterization of structural materials like metals and ceramics in extreme environments, integrating AI and robotics for accelerated materials research.

Contribution

The paper introduces AIMD-L, a novel automated laboratory with custom instruments and AI integration for high-throughput structural materials testing, focusing on extreme environments.

Findings

Data collection rates are 100-1000 times faster than traditional methods.

Automated workflows enable real-time data analysis and decision-making.

The system supports rapid iteration in materials development.

Abstract

Rapid developments in artificial intelligence and machine learning as applied to materials science are creating an urgent need for experimental data, which can be provided by high-throughput and autonomous laboratories. To date most demonstrations of such laboratories have focused on functional materials, with less attention paid to structural materials. We present here the Artificial Intelligence in Materials Design Laboratory (AIMD-L), an automated, high-throughput facility for characterizing the microstructure and properties of structural metals and ceramics, with an emphasis on materials in extreme environments. AIMD-L has two custom instruments for characterization of structural materials: HELIX for shock studies of materials, and MAXIMA for X-ray diffraction and X-ray fluorescence spectroscopy. Specifically designed for high-throughput studies, HELIX and MAXIMA are each capable of collecting data at rates two to three orders of magnitude faster than conventional systems. A third experimental station, SPHINX, is a commercial nanoindenter modified for integration into the automated workflow of AIMD-L. A user (which may be human or an AI agent) directs the experiments to be carried out by means of a centralized control program. The experimental stations are linked by a conveyance that moves samples around the lab, with a robot at each station for sample transfer in/out of the instrument. The experimental stations also communicate with a common data layer that streams data autonomously from each instrument to a data portal, where their arrival triggers automated workflows for data reduction and analysis. The processed data are immediately available to the human operator or agentic AI, forming a closed loop for rapid decision-making and experimental control.