Entropy governs the evolution of all systems, biological, nonbiological, and their hybrids, through interactions with their surroundings. Classical statistical mechanics defines entropy based on system configurations, leading to the Gibbs or Shannon information entropy. Zentropy theory expands this view by defining total system entropy as the sum of Gibbs/Shannon entropy and the statistically weighted average of the entropy of each configuration. When the configurations are accessible by quantum mechanics through density functional theory (DFT), zentropy provides an approach to predict emergent behaviors. For complex systems where DFT is intractable, zentropy enables an artificial intelligence (AI) framework to learn key configurations.

ZENtropy Journal provides an open-access platform dedicated to exploring complexity through the lens of entropy. It seeks to uncover how order arises from disorder and to illuminate mechanisms of transformation, adaptation, and organization across all domains of knowledge. The journal welcomes studies where configurations and their properties are predicted from DFT and computer simulations, learned through AI, or synthesized from hybrid approaches.