Machine Learning

Old treasure maps were drawn from hearsay: someone found gold by a river, someone else in the mountains, and each person dug in their own spot. High-temperature superconductors went the same way: cuprates were found in some compounds, iron pnictides in others, nickelates in yet others, and there was no common map.

In the Antarctic ice, a ghost particle leaves behind only a faint flash of blue light. The IceCube detector captures these flashes, and from them we need to infer where the particle came from. The PolarBERT model has learned to do this, but what exactly it sees inside has remained a mystery.

A protein in a cell doesn't hang motionless like a mannequin in a store window. Its atoms are constantly jiggling, stretching, and bending, like a plucked guitar string that keeps vibrating after you strum it. To understand how a protein works, you need to know how tightly each part holds onto its neighbors.

An interpreter gets distracted by every rustle in the hall, and a one-hour meeting stretches into two. Similarly, molecular foundation models learn the language of molecules from vast corpora, but each new conversation requires fine-tuning. Monroe changes the rules: it doesn't learn a new language but instantly finds common meaning from a few examples.

Hunting for a new material is like cooking without a recipe: you try, try again, and often throw things out. Previously, scientists manually flipped through papers and tested compositions one by one. Now there's a "robot chef" that reads the literature itself, mixes components, and checks what came out.

A perovskite solar cell is like a young athlete: it performs brilliantly at first, but tires with age. Over eight hours, its performance drops noticeably, with no visible cause like cracks or dust. Scientists took one cell and recorded its performance at 0, 90, 280, and 480 minutes of its life.

In 1998, mathematician David Boyd noticed something strange. He was studying the Mahler measure of polynomials, a quantity that gauges the "complexity" of their roots, and found that for a family of curves, it always equals a special rational number times the derivative of an L-function.

Galaxy NGC 4254 lies 1.5 million light-years from the center of the Virgo cluster. It drags a long stream of cooling hydrogen — a sign that it's plowing headlong into the hot gas. Losing cold fuel quenches the birth of new stars.

Four faint streaks on an image — that's all an astronomer sees. Like a sculptor reconstructing the shape of invisible glass from four shadows.

Two hundred forty-two thousand three hundred twenty-seven galaxies — like grains of sand on a beach. But astronomers turned this chaos into a map: the UMAP algorithm (a machine learning method) took from each galaxy eleven parameters — seven colors from optical to mid-infrared (that's photometry), magnitude…

Four faint streaks on an image — that's all an astronomer sees. Like a sculptor reconstructing the shape of invisible glass from four shadows.

Two hundred forty-two thousand three hundred twenty-seven galaxies — like grains of sand on a beach. But astronomers turned this chaos into a map: the UMAP algorithm (a machine learning method) took from each galaxy eleven parameters — seven colors from optical to mid-infrared (that's photometry), magnitude…

An artist makes a quick charcoal sketch, and the face in the portrait is already recognizable. Astrophysicists face a similar challenge: the spectrum of a star is its individual portrait, revealing temperature, composition, and gravity.

An artist makes a quick charcoal sketch, and the face in the portrait is already recognizable. Astrophysicists face a similar challenge: the spectrum of a star is its individual portrait, revealing temperature, composition, and gravity.

The Sun continuously scribbles magnetic fields across its surface. These patterns are not mere ripples but a handwriting in which nature has encoded warnings of flares. A flare is an explosion that releases the energy of billions of nuclear bombs in minutes, ejecting streams of plasma and radiation into space.

In combat, a pilot needs to instantly understand what an object is and what its intentions are. Conventional systems wait for complete radar data — like a goalkeeper who only reacts after the shot. The new algorithm works like an experienced goalkeeper: from the attacker's first moves, it predicts the target's maneuver.

Communicating with a satellite is like trying to hear a whisper in a storm: the signal drowns in noise. Engineers have created an AI algorithm that runs directly on the satellite and, with minimal power consumption, detects distortions more accurately than previous methods.

A typical tracking program resembles a navigator steering a ship by the stars. But as soon as the sky clouds over, coordinates drift, and entropy (a measure of uncertainty) builds up in the data. The new algorithm gets a ‘co‑pilot’ — a module that monitors how confident the main navigator is.

You're standing in the shower: you turn the tap, and the water is either freezing or scalding. The pipes have a mind of their own — the pressure jumps unpredictably. The new controller learns on the go: it doesn't know the plumbing layout, but within minutes it adapts and gently guides the temperature to comfort, without breaching safety limits.

The standard approach to a certain class of machine learning problems is like a spring that's been stretched and then forgotten: standard methods artificially inflate the problem's size, turning computations into a black hole for memory and time.

The brain communicates in short electrical clicks. Previously, to learn this language, thousands of examples with translations from the experimenter were needed. MOJO takes a different path: like learning a language through immersion, it first listens to the 'stream of consciousness' without any hints and finds recurring patterns on its own.

When a giant star collapses into a supernova or two neutron spheres merge in a cosmic dance, trillions of neutrinos carry away the cataclysm's energy.

Cooking a complex dish requires a clear recipe: first chop, then sauté, add spices at just the right moment. Chemical transformations also follow step-by-step recipes — mechanisms where atoms of carbon and hydrogen rearrange in a strict order. Previously, neural networks often invented nonexistent steps, like a whimsical cook swapping salt for sugar.

Researchers took 16 everyday activities — from washing dishes to squatting — and broke the movements down into elementary components, much like a spectroscope splits light into a spectrum. Comparing three approaches, they found an unexpected key to recognition: a single averaged snapshot is enough. Only nine joints out of dozens carried the crucial information — the rest hardly mattered.

Traditionally, machine learning algorithms operate in a static environment, like water flowing along a pre-cut channel without changing its shape. The new method flips this paradigm: it lets an agent simultaneously master actions and deliberately modify the very environment it inhabits.

An experienced detective doesn't calculate a criminal's path using the laws of physics: he checks a file of similar cases. The new prediction method works the same way: instead of complex equations, it has a vast archive of trajectories that have already happened — planetary motions, currency exchange rates, signals from space.

Predicting the properties of a new molecule is like guessing the flavor of an unfamiliar dish. The program works like an experienced chef: it “tastes” similar molecules whose properties are already known, and based on their “flavor,” it refines its guess.

An electron microscope is a giant pipe organ: instead of pipes (electromagnetic lenses, instead of air) a stream of accelerated electrons. It once merely painted mute frescoes of the atomic world, condemning scientists to grueling decipherment.

Machine learning has long ceased to be just a tool — it is becoming an investigator, independently fishing out the laws of nature from data. But how does a silicon mind come to understand?

Building a light chip is like a game where you have to guide a beam through a maze of tiny mirrors and beam splitters. Each element acts like a railroad switch: it either lets light pass straight through or directs it to an adjacent track. The goal is to produce a specific light pattern at the output while using as few 'switches' as possible. Usually, engineers spend years finding the shortest route for each task.

DFTB is a high-speed train in the world of quantum chemistry: hundreds of times faster than classical DFT, yet still on electronic rails. But its engine — iterative self-consistency of charges — sometimes resembles an orchestra where each instrument starts from an arbitrary note.

The quantum many-body world is a jazz ensemble, where each electron improvises, continuously listening to all the others, and the overall wave function is born from this tense dialogue.

The gamma sky, captured by the Fermi telescope, is a grand symphony of thousands of sources. Here, the rhythmic pulsations of pulsars, the powerful chords of active galactic nuclei, but most of all — unidentified objects, whose part remains a mystery.

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.

In the young Universe, about ten billion years ago, galaxies resembled endless fields after a thunderstorm: everywhere, giant — thousands of light-years across — clumps of star formation were boiling up, where young stars were born with unbridled energy. Today, these patches are an almost vanished species: in nearby galaxies, raucous hotbeds are rare.

DNA methylation consists of tiny chemical marks on our genes. Over the years, they change on a fairly strict schedule. Standard epigenetic clocks look at these marks and output a single number: biological age. But a number can't tell you how the pattern of marks rearranged day by day, year by year. The authors of this work decided to turn a static photograph into a continuous film.

Recommendation systems that decide which post you’ll see next run on deep neural networks made up of thousands of operations — intricate computation Graphs (blueprints of operations on data). These systems are living, ever-evolving Dynamical Systems: the slightest architecture change cascades through the entire graph.

In embryonic development, a cell resembles a musician in a vast orchestra: it must play its part, following a conductor — the gradient of signaling molecules. But the conductor is unreliable: morphogen concentrations constantly fluctuate. How then does a cellist not veer into a violin melody?

Almost a century after Фриц Цвикки suspected something was amiss in the motion of galaxies in the Coma cluster, and Вера Рубин measured stellar velocities in spiral arms, the nature of тёмной материи remains one of the most vexing mysteries.

Imagine an audition for a symphony orchestra, where out of hundreds of hopefuls, only those whose instruments sound pure and withstand the furious crescendo remain. In the world of materials, computers have learned to generate 'atomic scores'—thousands of virtual crystals that promise to become the foundation for quantum chips or superconductors.