AI gears up for data analysis: making the most of machine learning – Physics World

Applying AI know-how to the giant pool of data gathered from the world's leading and most powerful scientific instruments could accelerate the process of scientific discovery. Powerful machine-learning approaches offer new ways to extract scientific meaning from the raw experimental data, which ultimately could help funders to unlock more value from their investment in research. Large-scale experimental facilities such as neutron and synchrotron sources have become an essential element of modern scientific research, allowing visiting researchers to probe the structure and properties of many different types of materials. They also generate huge amounts of experimental data, which can make it difficult for visiting scientists without specialist knowledge of the experiment to extract meaningful information from the raw datasets. As a result, some of the data collected during their valuable beamtime is never properly analysed.