General Science
We aim to transform scientific research itself. Many scientific endeavors can benefit from large scale experimentation, data gathering, and machine learning (including deep learning). We aim to accelerate scientific research by applying Google’s computational power and techniques in areas such as drug discovery, biological pathway modeling, microscopy, medical diagnostics, material science, and agriculture. We collaborate closely with world-class research partners to help solve important problems with large scientific or humanitarian benefit.
Recent Publications
Expert evaluation of LLM world models: A high-Tc superconductivity case study
Haoyu Guo
Maria Tikhanovskaya
Paul Raccuglia
Alexey Vlaskin
Chris Co
Scott Ellsworth
Matthew Abraham
Lizzie Dorfman
Peter Armitage
Chunhan Feng
Antoine Georges
Olivier Gingras
Dominik Kiese
Steve Kivelson
Vadim Oganesyan
Brad Ramshaw
Subir Sachdev
Senthil Todadri
John Tranquada
Eun-Ah Kim
Proceedings of the National Academy of Sciences (2026)
Preview abstract
Large Language Models (LLMs) show great promise as a powerful tool for scientific literature exploration. However, their effectiveness in providing scientifically accurate and comprehensive answers to complex questions within specialized domains remains an active area of research. This work evaluates the performance of six different LLM-based systems for answering scientific literature questions, including commercially available closed models and a custom retrieval-augmented generation (RAG) system capable of retrieving images alongside text. We conduct a rigorous expert evaluation of the systems in the domain of high-temperature cuprate superconductors, a research area that involves material science, experimental physics, computation, and theoretical physics. We use an expert-curated database of 1726 scientific papers and a set of 67 expert-formulated questions. The evaluation employs a multi-faceted rubric assessing balanced perspectives, factual comprehensiveness, succinctness, evidentiary support, and image relevance. Our results demonstrate that RAG-based systems, powered by curated data and multimodal retrieval, outperform existing closed models across key metrics, particularly in providing comprehensive and well-supported answers, and in retrieving relevant visual information. This study provides valuable insights into designing and evaluating specialized scientific literature understanding systems, particularly with expert involvement, while also highlighting the importance of rich, domain-specific data in such systems.
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Observing long-lived longwave contrail forcing
Aarón Sonabend
Joe Ng
Atmospheric Measurement Techniques (2026)
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Contrail microphysical simulations and climate simulations have indicated that contrail cirrus cause a substantial fraction of aviation’s climate impact. While the approximations and parameter selections in these simulations have been well-validated over the past two decades, the heat trapping of contrails has not been observed using satellite data beyond a few hours. This is because contrails lose their linear shape after a few hours, making them difficult to distinguish from natural cirrus clouds. Here we provide satellite-driven analysis of long-lived heat trapping by contrails over North and South America. We aggregate a dataset of GOES-16 estimated outgoing longwave radiation and advected trace density of flight paths, and apply causal inference to discern the effect of contrails while controlling for radiative and cloud confounders. As a means of validation, we also generate synthetic datasets with known ground truth, and confirm that applying the causal inference method is able to recover the synthetic ground truth. Since this method yields an estimate which has some differences from both “instantaneous radiative forcing” (iRF) and “effective radiative forcing” (ERF) estimates which have been reported in the literature so far, we introduce the new term “observational radiative forcing, 12 hours” (oRF12). Our analysis estimates the longwave oRF12 from contrails over the Americas averaged 47.9 gigajoules per flight kilometer (95% CI: 31 to 52 GJ/km) during April 2019 to April 2020.
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Neural general circulation models for modeling precipitation
Stephan Hoyer
Dmitrii Kochkov
Janni Yuval
Ian Langmore
Science Advances (2026)
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Climate models struggle to accurately simulate precipitation, particularly extremes and the diurnal cycle. While hybrid models combining machine learning and physics have emerged with the premise of improving precipitation simulations, none have proven sufficiently skillful or stable enough to outperform existing models in simulating precipitation.
Here, we present the first hybrid model that is trained directly on precipitation observations. The model runs at 2.8 degrees resolution and is built on the differentiable NeuralGCM framework. This model is stable for decadal simulations and demonstrates significant improvements over existing GCMs, ERA5 reanalysis, and a Global Cloud-Resolving Model in simulating precipitation. Our approach yields reduced biases, a more realistic precipitation distribution, improved representation of extremes, and a more accurate diurnal cycle.
Furthermore, it outperforms the ECMWF ensemble for mid-range weather forecasting.
This advance paves the way for more reliable simulations of current climate and for the ability to fully utilize the abundance of existing observations to further improve GCMs.
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Leveraging LLMs to understand public perception of earthquake early warnings: A case study of the 2025 M6.2 Türkiye Earthquake
Hanjing Wang
Patrick Robertson
Richard Allen
Alexei Barski
Robert Bosch
Nivetha Thiruverahan
Youngmin Cho
Tajinder Gadh
Steve Malkos
Boone Spooner
Greg Wimpey
Marc Stogaitis
Scientific Reports, 16 (2026), pp. 22226
Preview abstract
Android's Earthquake Alert (AEA) system provided life-saving early warnings to millions during the M6.2 Marmara Ereğlisi, Türkiye earthquake on April 23, 2025. This event, the largest in the region in 25 years, served as a critical real-world test for smartphone-based Earthquake Early Warning (EEW) systems. The AEA system successfully delivered alerts to users with high precision, offering over a minute of warning before the strongest shaking reached urban areas. This study leveraged Large Language Models (LLMs) to analyze 560 public social media posts from the X platform, extracting 42 distinct attributes related to user experience and behavior. Statistical analyses revealed significant relationships, notably a strong correlation between user trust and alert timeliness. Key findings indicate that alerts received before shaking, clear information, and audible notifications significantly increased the likelihood of active protective responses and enhanced perceived usefulness and future trust in the system. Conversely, annoyance and perceived inaccuracy led to inaction and diminished trust. The study highlights the profound impact of smartphone-based EEW in mitigating seismic risk, providing actionable insights for optimizing alert design, public education campaigns, and future behavioral research to improve the effectiveness of such systems in seismically active regions.
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Performance and user response of Android's smartphone-based alerts in the 2025 Marmara Ereğlisi Earthquake
Patrick Robertson
Richard Allen
Alexei Barski
Robert Bosch
Nivetha Thiruverahan
Youngmin Cho
Tajinder Gadh
Steve Malkos
Boone Spooner
Greg Wimpey
Edward Shi
Marc Stogaitis
Nature Communications (2026)
Preview abstract
This study presents a comprehensive evaluation of Google’s Android Earthquake Alert (AEA) system during the Mw 6.2 Marmara Ereğlisi, Türkiye earthquake. AEA detected the event 5.31 seconds after its initiation, alerting over 16 million users. Warning times for weak shaking (MMI III) reached up to 150 seconds, with a median of 56 seconds. While near-source warning windows were shorter, the system achieved 90% true positives and 99% precision overall. The high density of the phone network enabled faster detection than traditional stations, even for this offshore epicenter. Feedback data shows AEA recipients were highly likely to take protective actions, such as "drop, cover, and hold on," or warn others. Timely alerts substantially increased user engagement, perceived usefulness, and future trust. These results highlight how crowd-sourced technology and behavioral insights can effectively enhance seismic resilience on a massive scale.
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Preview abstract
We compare two time-reversal-symmetric triangular-lattice Hubbard models at half filling: the zero-flux model and a staggered π-flux model with zero and π flux through oppositely oriented triangles. Although both reduce to the same Heisenberg model at leading order in the strong-coupling limit, their distinct charge fluctuation profiles yield different quantum paramagnetic regimes. Combining a strong-coupling expansion with infinite density matrix renormalization group (iDMRG) calculations, we find that the positive ring-exchange of the zero-flux model promotes spontaneous chirality, consistent with its notion of a chiral spin liquid at intermediate coupling. In the staggered-π-flux model, whose noninteracting limit is a Dirac semimetal, the ring exchange changes sign and disfavors chiral order and we find no resolvable scalar chirality. This renders flux-tuned ring-exchange an efficient discriminator between chiral and non-chiral quantum paramagnets at finite coupling.
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