Closing the synthesis gap in computational materials design

Data-driven strategies are reshaping computational materials design by accelerating the prediction of novel compounds with targeted functionalities. Beyond high-throughput screening, the integration of generative artificial intelligence enables exploration across vast chemical spaces comprising millions of known and hypothetical materials. This abundance of candidates presents a challenge: identifying which candidate compounds are not only low in energy, but also synthetically accessible. We assess advances towards closing this synthesis gap for inorganic crystals. These include the incorporation of thermodynamic potentials (from internal energies at 0 K to Gibbs free energies at reaction conditions), crucial for evaluating phase stability and reaction driving forces, chemical heuristics (from charge neutrality to electronegativity rules), and machine learning models (from positive–unlabelled learning to large–language models) to guide compound selection and prioritisation. Looking forward, the development of more robust synthesisability metrics, synthesis planning tools, and agentic workflows integrating experimental feedback will narrow the divide between virtual screening and real-world materials realisation.