Hybrid perovskites are the rising stars of photovoltaics: their efficiency is growing so fast it feels like we're on the brink of a solar revolution. But these materials, like intricate atomic mosaics, are extremely vulnerable — moisture, heat, and light quickly turn them to dust. Applying organic passivating molecules initially seemed like a panacea — an ultra-thin protective varnish that shields the surface. However, a paradox soon emerged: too much protection doesn't save, it destroys. The varnish starts dissolving what it was meant to strengthen. Why? The answer was hidden in the atomic ballet at the crystal's edge, and to glimpse it, a special interpreter was needed — a neural network potential trained like a polyglot.

Machine learning here works like a linguist mastering an unfamiliar language. First, the model absorbs the universal "grammar" of interatomic interactions — a vast corpus of structures from the `hyP-26` dataset, featuring carbon, oxygen, hydrogen, and silicon. Then comes fine-tuning on a specific "dialect": the coordination of the aminosilane molecule AEAPTMS on the perovskite surface. This two-stage approach, borrowed from large language models, cut the force prediction error in half, from 240 to 90 meV/Å, and avoided catastrophic forgetting, where accuracy plummets when encountering unfamiliar chemical contexts.

When the neural network "read" the coating dynamics, a drama of self-sabotage unfolded. Up to a concentration of 0.8 molecules per nm², the passivator lies flat, hugging the surface with multiple anchors and delicately filling defects. But crossing this Rubicon changes everything. Crowding forces the molecules to stand on end, like passengers in an overcrowded train car, compelled to squeeze vertically. Their long hydrocarbon tails, like elbows in a crowd, push the surroundings aside, penetrate the surface layer, and shatter the octahedral "bricks" of the crystal. Octahedral tilt angles increase, lead–halogen bonds lengthen — the structure strains at the seams. At maximum coverage, the fraction of distorted octahedra reaches 7.1%—enough to trigger a destructive chain reaction.

This atomic insight flips the search strategy. Now virtual screening of thousands of passivators is possible even before lab tests: we seek molecules with a perfect balance of steric effects — short and flexible enough not to crowd each other. In the future, integration with generative models will speed up the creation of self-adapting coatings, while accounting for oxygen-containing groups and hydrogen bonds will make numerical experiments more realistic. It's a step toward cracking one of the toughest nuts: predicting the long-term stability of functional materials under real-world conditions — from solar cells to catalysts and sensors on silicon substrates.