How Artificial Intelligence Becomes a Scientist's Partner ⚡ экспресс
The review examines the application of artificial intelligence in biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories, and unconventional computing. Common themes are highlighted: the need for diverse, reliable data; the development of transferable models of electronic structure and interatomic interactions; the integration of AI into end-to-end processes from simulations to experiments; and generative systems aimed at real synthesizability rather than idealized phases. It shows how large foundation models, active learning, and automated laboratories close prediction–verification loops while maintaining reproducibility and physical interpretability. The current state of AI-driven science, bottlenecks in data, methods, and infrastructure, and directions for creating more transparent and powerful systems that accelerate discoveries in complex real-world environments are outlined.
Science is a vast kitchen where scientist-cooks seek new recipes. Once, they sifted ingredients by hand; now artificial intelligence—a seasoned chef—lends a hand. AI memorizes millions of flavors and suggests surprising pairings: through information search, the algorithm uncovers hidden patterns. For instance, a neural network discovered an antibiotic in a couple of days—biologists would have needed years.
This approach also shines in light analysis of stars: AI seeks exoplanets by faint twinkles, as if spotting spices in a dish by hue. In materials science, algorithms propose new alloys, and robotic labs immediately fabricate them, closing the loop from idea to test. The key is to make AI explainable so scientists can follow its reasoning. Then discoveries that seem like science fiction will become routine.
🎯 A neural network once proposed a crystal structure thought impossible—and it was successfully created in the lab. Now that material is being explored for future electronics.
🎬 Replicator from Star Trek