Kan AI uppfinna nya material för att utöka det periodiska systemet ?

Nuvarande AI-system är mycket bra på att modellera hypotetiska kemiska strukturer och förutsäga stabila isotoper, men ingen kan "upptäcka" och namnge ett nytt grundämne på det formella IUPAC-sättet – grundämnen måste syntetiseras i acceleratorlaboratorier och verifieras genom upprepad experimentell observation innan de officiellt läggs till i det periodiska systemet. Nyligen utvecklade maskininlärningsmodeller (t.ex. GNoME) accelererar uppräkningen av tidigare okända stabila oorganiska föreningar, men dessa är utökade material snarare än nya grundämnen som skulle kräva ändringar av själva tabellen. AI förbättrar således upptäcktsprocesserna, men förblir ett assisterande verktyg; endast experimentell kärnfysik kan utöka det periodiska systemet.
KÄLLA: International Union of Pure and Applied Chemistry — https://iupac.org

— Uppdaterad 13 maj 2026

Background

Current AI systems excel at modeling hypothetical chemical structures and predicting stable isotopes, yet none can “discover” and name a new element in the formal IUPAC sense—elements must be synthesized in accelerator laboratories and verified through repeated experimental observation before official addition to the periodic table (International Union of Pure and Applied Chemistry — https://iupac.org). Recent machine-learning models (e.g., GNoME) accelerate the enumeration of previously unknown stable inorganic compounds, yet these are extended materials rather than new elements that would require altering the table itself. Thus, while AI augments discovery pipelines, it remains an assistive tool; only experimental nuclear physics can expand the periodic table.

Researchers use AI to screen potential new materials and predict their behavior under various conditions, which can help focus experimental efforts. AI can assist in the discovery of new materials by predicting their properties and behavior, but it cannot independently invent new elements to add to the periodic table. The process of discovering new elements involves complex experiments and verification by the scientific community. AI can, however, help scientists identify potential new materials and their properties by analyzing large datasets and running simulations. This can accelerate the discovery process, but human scientists are still necessary to design and conduct experiments to verify the existence and properties of new materials. The addition of new elements to the periodic table is overseen by the International Union of Pure and Applied Chemistry (IUPAC), which ensures that new elements meet strict criteria for recognition. AI's role in materials science is rapidly evolving, and it is likely to play an increasingly important role in the discovery of new materials in the future.