Topic: AI for Chemical Synthesis: Modern Digital Materials and Catalysis, Materials Design, Autonomous Discovery, and Failed Experiment

Artificial intelligence is transforming chemical synthesis from a predominantly trial-and-error discipline into an increasingly data-driven and predictive science. Its impact now extends beyond accelerating isolated computations or experiments, enabling the extraction of transferable structure–property–reactivity relationships, the navigation of high-dimensional chemical and materials spaces, and the integration of computation, synthesis, characterization, and decision-making within closed-loop discovery workflows. This Special Issue will highlight emerging advances at the interface of AI, materials chemistry, and catalysis, including digital materials and catalyst design, machine-learning-enabled reaction prediction, multimodal scientific agents, autonomous laboratories, and data-driven mechanistic discovery. Particular emphasis will be placed on the scientific value of incomplete, negative, and failed outcomes. Conventional chemical literature is strongly shaped by survivorship bias, whereas unsuccessful experiments and non-viable hypotheses often define the boundary conditions of reactivity, stability, and model validity. Systematically capturing such information is therefore essential for reducing redundant experimentation and for training robust, generalizable AI models. We welcome original research, reviews, perspectives, and methodological studies that advance predictive, interpretable, reproducible, and autonomous chemical discovery.