Searcharxiv⌕ Search

arXiv subjects

Hyun Sang Park

Publications and source records attributed to Hyun Sang Park.

2 recordsLinked to original sources

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

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.

cond-mat.mtrl-sci↗

Automated laboratory x-ray diffractometer and fluorescence spectrometer for high-throughput materials characterization

The increasing importance of artificial intelligence and machine learning in materials research has created demand for automated, high-throughput characterization techniques capable of rapidly generating large data sets. We describe here a new instrument for simultaneous X-ray diffraction and X-ray fluorescence spectroscopy, optimized for high-throughput studies of combinatorial specimens. A bright, focused, high-energy X-ray beam (24 keV) combined with a pixel array area detector allows spatially-resolved (~200 μm) transmission diffraction measurements through thick (100 μm) specimens of structural metals with exposure times as short as 1 s. Simultaneously, a silicon drift detector records X-ray fluorescence from the specimen for spatially-resolved measurement of composition. Specimen handling is fully automated, with a robot inside the X-ray enclosure manipulating the sample for measurements at different locations. Data orchestration is also automated, with data streamed off the instrument and processed autonomously. In this paper we assess the performance of the instrument in terms of throughput, resolution, and signal-to-noise ratio, and provide an example of its capabilities through a combinatorial study of Cu-Ti alloys to demonstrate rapid data set creation.

cond-mat.mtrl-sci↗