Data is being collected at an unprecedented rate, and this trend will only increase into the future. This data-centric paradigm shift is happening in all areas of society, and the chemical sector is no exception. Chemical data from a variety of sources is being collected from many areas of the sector, including research and development, business, scale-up, and production. Digitalizing this data (i.e., moving from pencil and paper lab notebooks to electronic lab notebooks, databases, and so on) offers many advantages, including providing a sustainable medium through which to store and reference information, preventing double-work, and offering the opportunity for data science.
This latter approach utilizes statistics to learn from the data and make predictions. Machine learning (ML) and artificial intelligence (AI) are powerful, emerging technologies that can take data and determine complex relationships between inputs and outputs, thereby offering a route to improve and optimize materials and chemicals.