Inverse Design of Inorganic Compounds with Generative AI
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 fo...
| Authors | Hannes Kneiding, Lucía Morán-González, Nishamol Kuriakose et al. |
|---|---|
| Primary category | physics.chem-ph |
| Published | 11 April 2026 |
| Last updated at source | 26 August 2026 |
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