Autonomous measuring instruments systematically detect new materials
Materials research
Peer-Reviewed PublicationIMAGE:
FELIX THELEN IS WRITING HIS DOCTORAL THESIS AT THE CHAIR OF NEW MATERIALS AND INTERFACES AT RUHR UNIVERSITY.
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CREDIT: © RUB, MARQUARD
Days or weeks to measure a sample
Despite highly specialized methods that can simultaneously produce a range of materials on a single sample and then measure them automatically, every minute counts when analyzing them: because days or even weeks can pass before the characterization of a sample is complete. The new algorithm can be integrated into existing measuring instruments to boost their efficiency significantly.
The measuring instrument itself searches for the next measurement area
“Through active learning, a measuring instrument is able to independently select the next measurement area on a sample, based on the information already available about the material,” explains Felix Thelen, developer of the autonomous measurement algorithm. In the background, a mathematical model of the measured material property is refined point by point until sufficient accuracy is achieved. At one point, the measurement can be stopped – and the results at the remaining measurement areas will be predicted by the generated model.
By analyzing ten materials libraries using electrical resistance measurements, the Bochum research team demonstrated how the algorithm works. “Our work is only just beginning at this point,” stresses Felix Thelen. “This is because in materials research there are far more complex measurement methods than resistance measurement, which also need to be optimized.” In cooperation with the manufacturers of the instruments, solutions must now be developed that enable the integration of such active learning algorithms.
JOURNAL
Digital Discovery
ARTICLE TITLE
Speeding up high-throughput characterization of materials libraries by active learning: autonomous electrical resistance measurements
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