Chemical Design by Artificial Intelligence

Empirical principles, and structure-property relations derived from chemical intuition, have driven for centuries the design of materials and molecules with desirable properties, and the identification of viable synthetic pathways. In recent years, thanks to the compilation of curated experimental and computational databases of compounds and reactions, and to the general advances in the application of machine-learning techniques to all fields of science, the design of molecules and materials has been increasingly led by data-driven approaches.

This special issue welcomes manuscripts that report novel methods and breakthrough applications to design chemical compounds and materials with improved properties, and synthetic routes to obtain them, by screening existing databases, by actively exploring chemical space, and by combining computational approaches with automated chemistry. Applications include, but are not limited to, discovery, computational or experimental characterization of catalysts and materials for energy storage and production as well as novel synthetic routes for molecules and materials.

Guest Editors: Daniel H. Ess, Kim E. Jelfs, and Heather J. Kulik with JCP Editor Michele Ceriotti