Session

DescriptionArtificial Intelligence (AI), data and knowledge representation are the key ingredients of the current revolution in many areas of science and engineering. This renaissance period is having important implications in chemical sciences too, with the development of new methodologies and practical solutions to solve secular problems such as - for example - the design of entire synthetic pathways in organic chemistry. At the core of AI is data, which is needed for training the different models. Data and the representation of its knowledge can be used to speed-up the development of customised solutions for daily industrial problems. In this minisymposium we wish to cover the relevant aspects of AI, data and knowledge representations for applications in chemistry, by bringing together world leading experts in the field of knowledge extraction and AI in chemical research. The topics will relate to the key advancement in chemistry produced by AI, the digitalisation and data availability across the chemical community and how existing AI models and knowledge representation technologies can be creatively exploited for bringing innovation in chemical science. The target audience is computational chemists and material scientists, cheminformaticians, and chemists who wish to grasp how AI will affect design and process in their field.