Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry
Austin M. Mroz, Annabel R. Basford, Friedrich Hastedt, Isuru Shavindra Jayasekera, Irea Mosquera‐Lois, Ruby Sedgwick, Pedro J. Ballester, Joshua D. Bocarsly, Ehecatl Antonio del Rio‐Chanona, Matthew L. Evans, Jarvist M. Frost, Alex M. Ganose, Rebecca L. Greenaway, King Kuok Hii, Yingzhen Li, Ruth Misener, Aron Walsh, Dandan Zhang, Kim E. Jelfs
Abstract
We offer ten diverse perspectives exploring the transformative potential of artificial intelligence (AI) in chemistry, highlighting many of the challenges we face, and offering potential strategies to address them.
Topics & Concepts
Transformative learningCross disciplinaryDisciplineFace (sociological concept)ChemistryEngineering ethicsNanotechnologyCognitive scienceData scienceComputer scienceSociologyEngineeringPsychologyMaterials scienceSocial sciencePedagogyMachine Learning in Materials ScienceComputational Drug Discovery Methods