Look out for potential bias in chemical data sets

There might be disadvantages to using tried and trusted methods.Credit: Science Photo Library Like most research fields, materials science has embraced'big data', including machine-learning models and techniques. These are being used to predict new materials and properties, and devise routes to existing drugs and chemicals. But machine learning requires training data, such as those on reagents, conditions and starting materials. These are usually gleaned from the literature, and are human-generated. The choice of reagents that researchers use could come, for example, from experience or from previously published work. It might be based on a recommendation passed from supervisor to graduate student, or simply on how easy reagents are to find or buy.