"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.
COMPAMED-tradefair.com; Source: Ruhr-University Bochum
