AI w nauce

AI for science · id aiscience · aliases: AI4Science, AI for Science, scientific machine learning, nauka wspierana przez AI

Zastosowania głębokiego uczenia do problemów naukowych w skali kwantowej, atomowej i ciągłej: biocząsteczki, chemia, materiały, klimat i równania fizyki.

Deep learning applied to scientific problems at quantum, atomistic and continuum scales: biomolecules, chemistry, materials, climate and physical equations.

In one sentence

Wykorzystanie uczenia maszynowego do odkryć w biologii, chemii, fizyce i naukach o Ziemi.

Using machine learning to make discoveries in biology, chemistry, physics and the Earth sciences.

Problem it solves

Eksperymenty i symulacje fizyczne trwają miesiącami i dużo kosztują, a możliwych cząsteczek czy materiałów jest więcej, niż da się sprawdzić.

Experiments and physical simulations take months and cost a lot, and there are more possible molecules or materials than anyone could ever test.

What it does

Model uczony na dawnych pomiarach działa jak szybki zwiad: wskazuje najbardziej obiecujących kandydatów, a laboratorium sprawdza tylko ich.

A model trained on past measurements acts like a fast scout: it points to the most promising candidates, and the lab only tests those.

Everyday example

Prognoza pogody na dziesięć dni policzona w minutę albo kształt nowego leku dopasowany na komputerze, zanim ktoś go zsyntetyzuje.

A ten-day weather forecast computed in a minute, or a new drug shaped on a computer before anyone synthesises it.

This concerns you if you…

pracujesz w laboratorium lub przy symulacjach naukowych
chcesz wiedzieć, skąd biorą się nowe leki i materiały
śledzisz, jak AI zmienia medycynę i energetykę
work in a lab or with scientific simulations
want to know where new drugs and materials come from
follow how AI is changing medicine and energy

Subtopics

Automatyzacja badań naukowych (AI-driven scientific discovery and research automation): Agentic LLM systems and optimisation loops automating the scientific cycle: hypothesis, plan, experiment (computational or robotic), analysis, report.
- Agenci generujący hipotezy i eksperymenty obliczeniowe (Hypothesis-generating research agents): Multi-agent LLM systems that generate, evaluate and evolve hypotheses and, in computational fields, also write code, run experiments and draft papers.
  - Ideacja naukowa przez analogie (Analogy-based scientific ideation): An agent system decomposes ideation into gap finding, analogy-based innovation and proposal writing, trained with reinforcement learning.
- Laboratoria autonomiczne (self-driving labs) (Self-driving laboratories): A closed design–make–measure–analyse loop: robotics runs experiments and a model (usually Bayesian optimisation, increasingly an LLM agent) chooses the next ones.
- Modele świata badań nad AI (Research world models for AI experiments): Models that predict outcomes of AI-model experiments from configuration representations, used to select promising training and inference variants.
Białka i biologia molekularna (Proteins and molecular biology): Machine learning for biomolecules: predicting structures and interactions, designing proteins, and language models of protein and genome sequences.
- Modele bazowe pojedynczych komórek (Single-cell foundation models): Transformers pretrained self-supervised on single-cell expression profiles, fine-tuned for annotation, integration and gene-network analysis.
- Modele językowe białek i genomów (Protein and genomic language models): Transformers and sequence models trained self-supervised on protein or DNA sequences; they yield embeddings, score mutations and generate new sequences.
- Projektowanie nowych białek (De novo protein design): Generating new protein backbones and sequences for a given function: target binders, symmetric assemblies and enzyme active sites.
- Przewidywanie struktury białek i kompleksów (Protein and biomolecular complex structure prediction): Predicting the 3D structure of proteins from sequence and, in newer models, of whole complexes with DNA, RNA, small molecules and ions.
- Zero-shot ocena skutków mutacji w białkach (Zero-shot protein variant effect prediction with language models): Zero-shot scoring of amino-acid substitutions by comparing variant and wild-type likelihoods under a protein language model.
Chemia i odkrywanie leków (Chemistry and drug discovery): Models for small molecules: property prediction, generating new structures, docking to proteins and synthesis planning.
- Dokowanie cząsteczek do białek (Molecular docking and binding prediction): Predicting a ligand's binding pose in a protein and its binding strength; classically search with a scoring function, now also generative models and whole-complex prediction.
- Generowanie nowych cząsteczek (De novo molecule generation): Generative models (SMILES language models, graph models, 3D diffusion) and reward-driven optimisation for proposing new compounds, including directly inside a protein's binding pocket.
- Planowanie syntezy wstecznej (retrosynteza) (Retrosynthesis planning): Searching synthesis routes from a target molecule back to available starting materials: networks proposing reactions plus tree search (MCTS, A*).
- Przewidywanie właściwości cząsteczek (Molecular property prediction): Regression and classification of physicochemical, biological and toxicity (ADMET) properties from a molecular representation.
Fizyka i astronomia (AI in physics and astronomy): ML in particle physics, astrophysics, cosmology and plasma physics: signal classification, fast simulation, inference and experiment control.
- Odsiewanie fałszywych alarmów w obserwacjach zmiennego nieba (Real–bogus classification of astronomical transients): Classifying difference-image cutouts from sky surveys (real source vs. artefact) with a convolutional network in the alert pipeline.
- Rozpoznawanie kształtów galaktyk na zdjęciach nieba (Galaxy morphology classification): Classifying galaxy morphology with convolutional networks (Bayesian ensembles) trained on vote distributions from crowdsourcing volunteers.
- Rozpoznawanie strumieni cząstek ze zderzeń (jet tagging) (Jet tagging in particle physics): Classifying hadronic jets (e.g. top, W, b, gluon) with permutation-invariant networks on particle clouds, graphs or transformers.
- Sterowanie plazmą w tokamaku przez uczenie ze wzmocnieniem (Reinforcement learning for tokamak plasma control): Deep RL trained in a plasma simulator that controls all of a tokamak's coils with one network, holding and shaping the plasma.
- Szybka symulacja detektorów modelami generatywnymi (Generative fast simulation of particle detectors): Generative models (GANs, VAEs, flows, diffusion, flow matching) replacing calorimeter shower simulation, evaluated on shared challenges.
- Wykrywanie fal grawitacyjnych przez sieci neuronowe (Deep learning detection of gravitational waves): Convolutional networks on interferometer time series that detect coalescence signals and estimate their parameters faster than matched filtering.
- Wyszukiwanie planet pozasłonecznych w zmianach jasności gwiazd (Exoplanet transit detection with deep learning): Classifying transit signals in light curves (planet vs. false positive) with 1D convolutional networks and ranking candidates.
Odkrywanie materiałów (Materials discovery): Machine learning for inorganic materials: predicting crystal stability and properties, learned interatomic potentials and structure generation.
- Generowanie nowych kryształów (Generative crystal and materials design): Generative models of crystal structures (mainly diffusion-based) conditioned on chemical composition, symmetry and target properties.
- Uczone potencjały międzyatomowe (Machine-learned interatomic potentials): Regressing atomic energies and forces from local atomic environments, trained on DFT data; replaces classical force fields in molecular dynamics.
Pogoda i klimat (Weather and climate modelling): Machine learning in the Earth sciences: global medium-range forecasts, ensemble forecasts, nowcasting and climate-model emulation.
- Globalne prognozy pogody z sieci neuronowych (Data-driven global medium-range weather forecasting): Deterministic models (GNNs, transformers) trained on reanalysis that autoregressively forecast the global atmospheric state 1–15 days ahead.
- Modele hybrydowe i emulatory klimatu (Hybrid and learned climate models (climate emulators)): Learned emulators of general circulation models and hybrids that pair a differentiable dynamics solver with networks parameterising sub-grid processes.
- Probabilistyczne prognozy pogody (zespoły) (Probabilistic ensemble weather forecasting): Generating forecast ensembles with probabilistic (mainly diffusion) models instead of a single deterministic forecast.
- Prognozy opadów na najbliższe godziny z radaru (nowcasting) (Precipitation nowcasting from radar): Short-range (5–90 min, up to a few hours) precipitation forecasting from radar image sequences with generative or convolutional models.
- Zwiększanie szczegółowości prognoz i projekcji klimatu (downscaling) (Machine-learning downscaling of weather and climate data): Mapping coarse fields from global models or reanalysis to high resolution with regression and generative networks (e.g. residual-correcting diffusion).
Uczenie maszynowe z fizyką i symulacje (Physics-informed and scientific machine learning): Surrogate models and solving partial differential equations with neural networks, with physical knowledge built into the loss, architecture or data.
- Operatory neuronowe (FNO, DeepONet) (Neural operators (FNO, DeepONet)): Networks learning mappings between function spaces (e.g. initial condition → PDE solution), largely independent of mesh resolution.
- Sieci uwzględniające fizykę (PINN) (Physics-informed neural networks (PINN)): A network approximating a PDE solution, trained with a loss combining data fit, the equation residual and boundary conditions.

Sources

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems · Xuan Zhang, Limei Wang i in. (zespół kilkudziesięciu autorów, m.in. Shuiwang Ji)
Computational protein design and protein structure prediction win Nobel Prize in Chemistry (EMBL) · EMBL