Inverse Design of Inorganic Compounds with Generative AI

TL;DR

This paper reviews how generative AI is advancing inverse design in inorganic chemistry, addressing complex challenges in representing diverse inorganic systems for compound generation.

Contribution

It analyzes recent progress in generative AI methods tailored for inorganic compounds, emphasizing data representation and future research directions.

Findings

Generative AI methods have evolved to handle inorganic compounds' complexity.

Focus on data-representation-model pipelines for inorganic systems.

Discussion on future benchmarks and synthesizability metrics.

Abstract

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.