Your new furniture probably made the air in your living room more toxic than a busy highway. That sharp, eye-watering smell from pressed-wood cabinets and laminate flooring is formaldehyde, a carcinogen the World Health Organization has flagged as a serious indoor air threat. Now, a team of French and Tunisian researchers thinks a cheap, abundant element could hold the key to scrubbing it out of the air at room temperature.
Gregory Bassen had a simple question: could a large language model write a recipe for making a ceramic oxide that actually works in a real lab? Not in theory. Not in simulation. In a furnace, with real powders, producing a material you could hold in your hand.
Inside every living cell, enzymes are running the chemistry of life, snipping, splicing, and assembling molecules with a precision that industrial chemists can only dream of. A single enzyme can catalyze a million reactions per second. The problem? Understanding how they do it has been painfully slow. Even with GPU-accelerated quantum chemistry, simulating a single step in an enzymatic reaction on a cluster of 300 atoms can take hours. Mapping a full reaction pathway takes days.
Now researchers at MIT have built something that changes the math entirely.
Heather Kulik’s group released Enerzyme, an open-source framework that trains neural networks to simulate enzyme catalysis with near-quantum-chemical accuracy, but at a fraction of the cost. The key breakthrough isn’t a better algorithm. It’s that Enerzyme handles everything that makes enzymes uniquely hard to model: their size, their solvent environment, and the complex charge transfers that drive their chemistry.
Imagine being handed a box containing more objects than there are stars in the observable universe — and being told that somewhere inside it lies a material that could make batteries last ten times longer, or a molecule that kills antibiotic-resistant bacteria. You get to test a few hundred items before your budget runs out. Good luck.
That, roughly speaking, is the problem facing chemists who search for new molecules. The space of synthesizable chemical compounds is estimated to exceed 10⁶⁰ candidates — a number so large it mocks the very idea of systematic search. For decades, discovering a useful new molecule has been a mixture of chemical intuition, educated guesswork, and sheer persistence. Now, a team from Los Alamos National Laboratory and Georgia Tech has built an AI system that learns how to search — and it’s dramatically better than anything we’ve had before.