AI tool can identify chemical signatures of alien life

A new artificial intelligence system takes on one of science’s toughest questions: did this chemistry originate from life or not?

Built by researchers at Georgia Tech and NASA’s Goddard Space Flight Center, the technology studies meteorites and Earth soils for patterns that might signal biology.

The system, called LifeTracer, is a machine learning tool that compares complex mixtures of organic molecules.

In tests, LifeTracer correctly distinguished lifeless space rocks from life-bearing Earth samples about 87 percent of the time, using only chemical data.

Why alien chemistry confuses us

To build that training set, the team led by Amirali Aghazadeh, an assistant professor of electrical and computer engineering at Georgia Tech, measured soluble organics in eight carbon-rich meteorites and ten terrestrial soils and shales.

The experts relied on mass spectrometry, a technique that identifies molecules by their mass and fragment patterns.

Space rocks and Earth soils both carry rich organic chemistry, so telling life derived molecules from raw chemistry is not as simple as checking one compound.

Some abiotic organics, made by nonliving processes in space or on rocks, include amino acids and nucleobases that earlier work has already found inside carbon-rich meteorites.

LifeTracer turns peaks into patterns

Instead of chasing single molecules, LifeTracer reads the forest of signals that mass spectrometers record as peaks in two-dimensional chromatograms.

In this study, the team logged 9,475 distinct fragment peaks in the meteorite extracts and 9,070 in the terrestrial samples.

From those peaks, the software builds thousands of features that encode each compound’s mass and how long it stayed in each column of the gas chromatograph.

A logistic regression, a statistical method that estimates probabilities between two options, acts as the core classifier that assigns each sample to either the abiotic or biotic class.

Building chemical fingerprints fast

Because many of those peaks come from fragments of the same parent molecule, the researchers clustered them into groups that share similar retention times.

Each group acts like a chemical fingerprint, marking a family of molecules that tends to appear in either space rocks or Earth materials.

The algorithm then learns which fingerprints push a sample toward the abiotic label and which ones push it toward the biotic label.

Once that training is complete, the model can examine a completely new pattern of peaks and decide which side it most closely resembles.

Clues buried in ancient space rocks

Many of the meteorites in the dataset belong to carbonaceous chondrites, older stony meteorites rich in carbon and preserving early solar system material.

Earlier work has shown that such meteorites can hold tens of thousands of distinct organic molecules, far beyond what older analytical methods could capture.

In LifeTracer’s analysis, fragment ions from meteorites tended to leave the chromatograph’s first column earlier than those from Earth samples. That pattern means the abiotic mixtures were overall more volatile, so their molecules moved faster through the heated columns.

Chemistry that represents life

Among the most informative fingerprints were polycyclic aromatic hydrocarbons (PAHs), ring-shaped molecules built from linked carbon atoms and hydrogen that often arise in high-energy environments.

One simple member of that family, naphthalene, showed up repeatedly in meteorite samples and became the strongest single predictor of an abiotic origin in the model.

On the Earth side, one especially telling fingerprint came from a benzene ring with multiple long carbon branches, found mostly in soils from places like Utah, the Atacama, and Iceland.

Their structure matches the kinds of long, branched molecules that modern life uses in cell membranes, so their presence boosted the chance that a sample was labeled biotic.

Where the patterns hint at life

Scientists call the chemical clues that might point to life biosignatures, signs that past or present life shaped a planet’s chemistry.

Many teams now argue that the most reliable biosignatures will come from whole patterns of organics rather than from a single special molecule.

“Determining whether organic molecules in planetary samples originate from biological or nonbiological processes is central to the search for life beyond Earth,” said Daniel Saeedi, the study’s lead author at Georgia Tech.

His team designed LifeTracer specifically to work with noisy, incomplete datasets like those planetary missions are expected to return.

LifeTracer for future space missions

Sample return missions from asteroids and planets carry only tiny amounts of material, often just grams of dust and rock scraped from carefully chosen sites.

Those samples mix organics from many sources, including space chemistry, surface weathering, and any biology that might once have lived there.

Future projects such as a Mars Sample Return campaign and Japan’s Martian Moons Exploration mission aim to bring back material from places that may once have been habitable.

Tools like LifeTracer could help mission scientists quickly sort those mixtures, flagging which ones most closely resemble life-modified chemistry instead of purely abiotic baselines.

Finding new clues about life

Because it works on the full distribution of molecules rather than a short list of known biomarkers, LifeTracer could also catch unfamiliar kinds of chemistry that still look organized like life.

That broad view will not by itself prove life existed elsewhere, but it can guide closer, slower analyses that dig into specific molecules and isotopes.

If scientists can combine pattern-finding tools like LifeTracer with agents that propose and test hypotheses, they may finally have a practical way to search huge datasets for subtle signs of biology. 

That combination might let future missions treat every returned grain of dust as a potential clue to how life starts, both on Earth and far beyond.

The study is published in the journal PNAS Nexus.

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