Researchers in Japan have developed a method to automate a mathematical procedure used to analyse X-ray diffraction data, potentially providing a means to save materials scientists time and expenditure.
Researchers in Japan have developed a method to automate a mathematical procedure used to analyse X-ray diffraction data, potentially providing a means to save materials scientists time and expenditure.
Rietveld analysis is frequently employed for data tasks and involves firing X-rays at a crystal that interact with the geometric arrangement of its particles and are diffracted in many directions.
The resulting pattern of rays depends on the crystal’s precise structure and is key to identification. However, while this is effective and often used process for revealing the three-dimensional atomic structure of new materials, the patterns’ complexity requires expert assessment as does the intensity of the diffracted X-rays before it is possible to determine accurately the crystal’s internal arrangement.
Now Japan’s National Institute for Materials Science (NIMS) has outlined in Science and Technology of Advanced Materials: Methods how it has harnessed robotics and machine learning to reduce labour intensity and circumvent risk of human error.
“We have developed a robotic process automation (RPA) system that we apply to an existing Rietveld analysis program called RIETAN-FP,” explained spokesperson Ryo Tamura of the NIMS team.
“By using our new procedure, with the help of machine learning, we have succeeded in performing Rietveld analysis automatically,” Tamura adds.
The automation can be run on a personal computer and can reduce human error as well as greatly speed up the data analysis.
Tamura said the method makes use of graphical user interface (GUI) applications and machine learning ability to automatically design and analyse materials with little human involvement.
To test the accuracy of their procedure the team analysed samples of powdered compounds whose crystal structures were known, as Rietveld analysis itself can determine the structures this way, without the need to grow large single crystals.
The researchers aim to refine their procedure for more complex crystal structures and apply machine learning RPA strategy for other materials science applications such as simulation methods used for calculating material properties or controlling experimental equipment.
“Automating Rietveld analysis brings a very powerful new tool into the entire field of materials science,” stated Tamura.
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