Inverse Design of Inorganic Compounds with Generative AI
For chemists and materials scientists, this review provides a comprehensive overview of generative AI approaches for inorganic compound design, highlighting current limitations and future needs.
This review analyzes how generative AI methods have been adapted for the inverse design of inorganic compounds, addressing challenges such as chemical composition, geometry, symmetry, and electronic structure. It discusses the evolution of data-representation-model pipelines and future directions like benchmark standardization and synthesizability metrics.
Machine learning is revolutionizing chemistry. Beyond the value of predictive models accelerating virtual screening, generative AI aims at enabling inverse design, reversing the compound-to-property prediction paradigm into property-to-compound generation. Chemists now have access to a rich AI toolbox for organic chemistry, including drug discovery. However, the application of these methods to inorganic compounds remains limited by the challenges posed by their intrinsic nature. This Review analyzes how these challenges have been addressed, considering widely diverse systems ranging from molecules to crystals, including transition metal complexes and microporous materials. The analysis focuses on how generative AI methods have evolved towards data-representation-model pipelines that address the full complexity of inorganic compounds, including their chemical composition, geometry, symmetry, and electronic structure. Future directions, like benchmark standardization and the development of synthesizability metrics, are also discussed.