AI models are dreaming up the materials of the future

Subscribe to enjoy similar stories. SCIENTISTS LOOKING to remove carbon dioxide (CO2) from the air cleanly and cheaply have long been interested in metal-organic frameworks, or MOFs: gigantic, sponge-like molecules that can be precisely engineered to capture the gas and then release it on command. Made of metal ions held together by compounds containing carbon, MOFs come in a dizzying array of structures, each with its own distinct properties.

A MOF capable of absorbing CO2 at a humid sea-level location, for example, will have a different structure from one that can operate in a dry, high-altitude climate. Sorting through the billions of possibilities to find the right MOF for the job is an almost impossible task for a human chemist. It is, however, a perfect task for an artificial-intelligence (AI) model.

One startup that is building such a system is CuspAI. It uses a multitude of AI models in concert: some are trained to generate candidate molecules with prescribed properties, which get passed to a specially trained foundation model to assess their properties. CuspAI’s goal isn’t simply to find a good MOF, but to build a system that can spit out the right one for any environmental conditions—and, from there, to demonstrate that AI can be used to tackle any problem in materials science.

Better batteries, cleaner bioplastics, more powerful semiconductors and, potentially, even room-temperature superconductors might soon be up for grabs. This is no pipe-dream. In a conference paper in November 2024, Aidan Toner-Rodgers, a doctoral student in economics at the Massachusetts Institute of Technology (MIT), analysed the effects of a new AI tool on the productivity of materials researchers at an unnamed American company.