machine learned interatomic potential

Imagine a book with latches on its pages: you can only flip a page if you know how to click its edge. That is roughly how the mechanical metamaterial we are about to discuss works. Each cell is a small mass inside a larger one, connected by an elastic beam. The beam can sit in two stable positions, like a switch.

A cartographer who draws not mountains and rivers but invisible bonds between electrons: that's one way to describe the new neural network. Instead of atomic coordinates, it takes molecular orbitals and draws a graph where nodes are orbitals and edges are measures of their quantum entanglement.

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.

A fusion reactor is like a super-hot furnace where plasma reaches millions of degrees. The walls are made of tungsten, the most refractory metal there is. But even tungsten needs protection: fast particles from the plasma bombard the surface and knock atoms loose. These ejected atoms enter the plasma and cool it down, like a spoonful of cold milk in hot tea.

In a pot of water, microscopic spheres, colloidal particles, are floating. Each sphere is coated with long polymer chains that look like a brush and carries an electric charge. If you get the temperature and solution composition right, the spheres start arranging themselves into ordered patterns — crystals.

When sugar goes into tea, it dissolves. But why doesn't it just fall back out? It's all about chemical potential — a quantity that shows how 'favorable' it is for a substance to be in a given place and state. For a pure substance, it's easy to calculate, but for a mixture of several components, it's a real puzzle.

A driver on a slippery road doesn't know exactly how the car will respond to the steering wheel, and learns on the go, leaving room for error. A smart control algorithm behaves in a similar way.

Power systems with solar panels and wind turbines are like a chess game: cloud cover and load changes are the opponent's moves, impossible to foresee. To maintain balance, operators used to run through dozens of scenarios (uncertainty) — that took too much time and computing power.

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?

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.

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.

Inside a fusion reactor, plasma rages — a gas heated to millions of degrees. There are no direct windows, and cameras and sensors quickly fail. Engineers see only fragments of information: a few sensors at scattered points. It's like trying to grasp a symphony from the sound of three microphones in a vast hall.

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.

Quantum spin chains resemble a line of people passing along a secret handshake: each turns to a neighbor, and how firmly they clasp palms determines how the wave runs through the entire chain. In the ideal Heisenberg chain, quantum magnetism’s bedrock, all handshakes are equal.

Once Richard Feynman saw in quantum systems a challenge for classical computers. Now we face the inverse problem: understanding what exactly the quantum processor has simulated. Among the states it births are veritable 'black boxes'—topological phases of matter.

Today's Freon-based refrigerators and air conditioners silently poison the atmosphere. An alternative is solid-state caloric materials that cool under rapid pressure changes via the barocaloric effect.

Computational studies of material behavior have long suffered from two extremes: either fast but inaccurate models, or accurate but slow physical calculations. Modern programs, trained on data, have nearly resolved this dilemma — now the bottleneck is not computing power, but human routine: setting up simulations and checking results. Paimon works like an autonomous chef.

A chef doesn't waste ingredients cooking the whole dish to check the taste: they just taste a drop of sauce. The same logic underlies the "information matching" method for predicting metal strength. Instead of expensive calculations of every atom's behavior, scientists "taste" only simple indicators linked to plasticity — a metal's ability to bend without breaking.

A plumber clears a clog in a narrow, winding pipe — by feel, without seeing inside. That's roughly how a surgeon feels during mechanical thrombectomy, the operation to remove a blood clot from a brain vessel. Time is short, and every move can either save a life or damage the vessel wall.

A photographer shoots a night forest, trying to catch fireflies in the frame. The flashes are rare and faint, while around there's the noise of wind and distant lights.

In the orchestra of atoms, each instrument has its own score. But sometimes two musicians start playing in unison, and the sound amplifies many times over. In the quantum world, this 'harmony' is called a Feshbach resonance. It allows you to control atomic interactions with jeweler-like precision.

Imagine a factory where hundreds of tiny assembly lines run nonstop. Each line is a molecular motor that converts chemical fuel into motion. To make the motor go faster or in the right direction, an engineer tweaks the settings — the shape of parts, the strength of interactions.

Quantum calculations are a recipe with perfect ingredients, but nature-the-chef cooks in her own way. Machine-learned models of atoms memorize the recipe and predict material properties in seconds instead of weeks.

A uranium nucleus is like a tight wallet snapped shut on two halves. To make it burst and release energy (that is, to make the nucleus split) you have to climb over an energy barrier. The height of this threshold determines whether a substance is stable or ready to fall apart at any moment.

A neural network trained on collisions of protons and electrons transferred its skills to a completely different experiment — catching neutrinos. Like a chef who learned French cuisine and then, without preparation, baked an excellent Uzbek plov.

In collisions of heavy nuclei at colliders, quark-gluon plasma is born — a state of matter where quarks and gluons are as free as water in steam. Heavy quarks, such as b-quarks, entering this plasma lose energy, and their bound states, bottomonia, are destroyed.

Inside every cell, there's a network of thin tubes — microtubules. They act as both scaffolding and rails: they hold the cell's shape and transport cargo. These tubes are built from the protein tubulin and naturally glow faintly under ultraviolet light. Scientists wondered: can we make this glow stronger or weaker on demand? The key to control is the amino acid tryptophan.

Heat flows through matter like water through pipes: sometimes fast, sometimes barely. Engineers seek materials that almost don't let heat pass — like clogged pipes. Such insulators are vital for energy saving and devices that turn heat into electricity. The neural network KappaFormer quickly evaluates the "throughput" of an atomic structure.

When a neutron or proton enters an atomic nucleus, it behaves like a ball hitting a rough wall: it can bounce off in different ways, depending on where it hits.

The atomic nucleus is like a musical box: inside, nucleons (protons and neutrons) move along their orbits, like pins plucking the comb. But in some nuclei, such as magnesium-32, this box plays a tune you wouldn't expect from the usual shell-filling scheme.

Next-generation batteries don't just use a liquid electrolyte; they use water with a huge amount of salt dissolved in it — lithium ions and bulky organic molecules. There are so many ions that they constantly collide, like a crowd in a narrow corridor. Tracking every ion in such a crowd is no easy task.

A seven-metal alloy is like a team where every player is pulling the blanket their own way. Atoms of iron, cobalt, nickel, aluminum, titanium, tantalum, and vanadium can either fall into a strict pattern or scatter into random positions. This determines whether the material will be as strong as steel or as light as aluminum.

Inside every battery sloshes a liquid electrolyte, and how ions jostle within it determines how much energy the battery delivers and how long it lasts. Previously, peeking into that crowd meant either running long experiments or launching ultra-precise calculations that devour weeks of computer time.

A handful of sand on a table is just a pile. But if you shake the grains just right, the pile starts behaving like a memory: it remembers which motion follows which. In the new experiment, particles were moved cyclically, and after training, they reproduced the desired pattern even without a full command. The secret lies in the contacts between particles.

In ping-pong, two paddles connected by an invisible thread feel each other's movements, guessing the ball's trajectory. Researchers gave a computer a similar thread — quantum entanglement. Into an ordinary trial-and-error learning program, they embedded a quantum circuit of eight qubits — particles that can spin like a coin on its edge.

In a regular battery, the electrolyte is liquid, and charged particles (ions) move freely. In the solid-state version, they squeeze through atomic 'corridors', like pedestrians in narrow passages. For a battery to work quickly, you need a material with high ionic conductivity.

A chunk of nuclear fuel inside a reactor is not just a solid. When heated, the uranium atoms in it start to 'wiggle' their magnetic moments, and this motion changes thermal conductivity and other properties.

Tearing apart an aluminum cube just a few nanometers wide is like trying to watch a droplet fall in slow motion when the camera itself is firing at machine-gun speed. Atoms inside the metal vibrate trillions of times per second, while real deformation in the lab takes minutes.

Testing molecules for solar cells is like trying a new sauce in a restaurant where the chef changes every five minutes. Disturbances (humidity, substrate cleanliness, deposition method) hide the true effect. To extract real benefit from noisy data, scientists created a 'smart sieve'—an algorithm that sifts through the results and leaves only reliable improvements.

Before every tokamak startup, a device where superheated gas is held by magnetic fields, operators spend hours calculating how the plasma will behave. Mistakes are too costly: one wrong parameter can ruin the experiment.