AI 是如何被用在科学研究中的？How AI Is Put to Work in Scientific Research

Proteins that once took years to map now resolve in seconds; the expansion of the cosmos can be simulated on a chip. Artificial intelligence has redrawn what scientific discovery looks like — and this is how the field actually puts it to use.

Chatbots that draft emails and generators that make pictures are only the visible surface. The larger story is unfolding where few outsiders look: inside university labs, national observatories and the R&D wings of big pharma. Automation was AI's first job in these places; today it functions as an engine of discovery in its own right.

What makes this possible? The leverage comes from three capabilities no human team can match: swallowing datasets of a size that defies intuition, spotting regularities hidden inside chaotic systems, and running simulations of intricate physical processes so quickly that work once measured in millennia now takes minutes. Mapping a protein that might yield a cure, or replaying the merger of two black holes — either way, the frontier of knowledge moves faster.

01Medicine and Drug Discovery: Getting to Treatments Sooner

Nowhere does AI's contribution save more lives than in biology and medicine. The traditional route to market for a new drug runs past a decade and swallows billions of dollars. AI is squeezing that schedule hard, working as a skeleton key that opens the molecular world.

AlphaFold was the turning point, cracking a "protein folding problem" that had stood for 50 years. With 3D structures for virtually every known protein now predicted, researchers hold something like an atlas of life's building blocks. Mapping, though, is only the start. Contemporary models go further and simulate the way a prospective drug molecule would dock onto a target. The mechanics behind that kind of deduction — how a system works through logical problems in several steps — is the subject of our piece on reasoning AI.

What Comes After Protein Folding

• Virtual Clinical Trials: "in silico" patient groups are generated by AI, letting researchers see how a compound behaves inside a human body and cutting back on early animal testing.
• Repurposing Existing Drugs: algorithms trawl the records of already-approved medicines for fresh applications, sidestepping the years that safety testing would otherwise demand.
• Personalized Medicine: by reading a patient's own genomic profile, AI estimates which treatment is likely to work best — a departure from the "one-size-fits-all" model.

02Modelling the Climate and the Environment

Billions of variables interact in the climate, and the system is both chaotic and non-linear. Conventional models built on physics burn a great deal of computation and are often poor at saying what will happen in one specific locality. AI changes the odds. Trained on decades of satellite imagery, buoy readings from the oceans and atmospheric records, it now calls weather patterns and climate shifts with accuracy never seen before.

Work is under way on "Digital Twins" of Earth: intricate virtual stand-ins that churn through millions of runs to show what various carbon-emission pathways would mean across the coming century. Judging how trustworthy such planet-scale models are calls for disciplined AI benchmark testing, so that what they predict stays consistent with real physics.

03Physics and Astronomy: Searching a Cosmic Haystack

The volume of data modern physics throws off exceeds anything a human workforce could ever sift. At CERN, the Large Hadron Collider (LHC) emits petabytes per second out of its particle collisions. Instruments such as the James Webb Space Telescope (JWST), along with the Vera Rubin Observatory due to come online, gather cosmic imagery on a scale beyond imagining. Take AI out of the picture and the most consequential findings would stay lost in the noise.

Algorithms serve as the finest filter available. Astronomers point machine learning at millions of star images, hunting exoplanets through the faint, repeating dip in brightness when a planet crosses in front of its sun. In particle physics, deep learning rebuilds the trajectories of subatomic particles out of collision fragments, a step toward identifying dark matter and undiscovered fundamental forces. AGI (Artificial General Intelligence) remains out of reach, yet within their narrow slice of data analysis these purpose-built systems already outperform people.

04Chemistry and Materials: Engineering What Comes Next

New materials have powered every great step in human technology, from the Bronze Age through to the Silicon Age. What AI changes is the method: guesswork gives way to deliberate design. Generative models can now conjure chemical compounds with no counterpart in nature and tune them for whatever property is wanted — conductivity, strength, heat resistance.

The green transition hangs on work like this. AI is being pointed at solid-state battery electrolytes that charge in less time and will not catch fire, and at catalysts able to strip carbon dioxide straight from the air at useful efficiency. For a view of where these findings are being pushed furthest, follow the latest breakthrough AI research emerging from leading university laboratories.

Applications Already in Play

• Superconductors: the hunt is on for a substance that carries current at room temperature with no resistance at all — a find that would remake national power grids and quantum computing alike.
• Plastic Alternatives: algorithms single out biodegradable polymers that prove as durable as the plastics we already use, yet leave no environmental damage behind.
• Solar Panels: in next-generation cells, AI fine-tunes perovskite crystal structures so that more light is absorbed and efficiency climbs.

05AI as Co-Scientist: Self-Driving Labs and Fresh Hypotheses

The "AI Co-Scientist" has arrived. Analysing data is no longer the whole of its remit — it now takes part in the scientific method directly. Capable agents get through thousands of papers, spot where existing knowledge has holes, and put forward original hypotheses that can be tested.

More striking still, these systems are being wired into robotic "self-driving labs." The AI drafts an experiment, tells the robots to combine the chemicals and carry out the tests, reads the outcome, then drafts the following experiment — with nobody stepping in. Running around the clock in a closed loop, the setup speeds material discovery by orders of magnitude. For up-to-date coverage of these autonomous systems and how quickly the area is advancing, see AI research this week.

06Milestones on a Timeline

Science absorbed AI in a remarkably short span. The milestones below trace the path to the present.

1. 2020

   AlphaFold Cracks Protein Folding

   With atomic-level accuracy, DeepMind's AI produced 3D protein structures — closing out a grand challenge that had stood in biology for 50 years.

2. 2022

   New Battery Materials Found by AI

   Generative AI is used to screen 32 million inorganic materials, from which 18 promising solid-state battery candidates emerge.

3. 2023

   The First Antibiotic Designed by AI

   At MIT, machine learning turns up a new antibiotic compound that works against drug-resistant superbugs such as MRSA.

4. 2024

   An AI Achievement Wins the Chemistry Nobel

   The Nobel committee recognises foundational contributions to AI-driven structural biology and to computational protein design.

5. 2026

   Labs That Run Themselves

   Labs run end-to-end by AI agents start turning up new carbon-capture catalysts with no human in the loop.

07Common Questions, Answered