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Research collection

AI for Science research

Research applying AI to scientific discovery, simulation, and modeling. Explore reported results with attention to experimental evidence and domain constraints.

43 papers · Latest edition October 5, 2026

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Three of the latest briefs in this collection. Read the evidence and the original papers alongside them.

All AI for Science papers

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01Scientific Ai

AI-guided high-throughput discovery of iridium- and ruthenium-free palladium-oxide catalysts for durable acidic oxygen evolution

Ken J. Jenewein, Faezeh Habib Zadeh, Xiaoxiao Wang, Gustavo Malkomes, Huafan Zhang, Natalie Page, Jae Jin Bang, Peter J. Santiago, Karla V. Contreras, Katherine K. Li, Allison Perna, Lorena M. Britton, Fahrettin Kilic, Kevin J. Cruse, Armin Taheri, Krishnanand Mallayya, Harley Quinn, Rebecca A. Durr, Peter A. Beaucage, John M. Gregoire, Rafael Gómez-Bombarelli

An AI-guided robotic lab discovered palladium-based catalysts that could make acidic water electrolysis more durable while reducing dependence on scarce iridium and ruthenium.

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03Scientific Ai

EurekaBench: Measuring Agentic Ability to Discover New Scientific Insights

Jiayi Geng, Zhengxuan Wu, Kevin S. Chen, Seungone Kim, Joseph Janssen, Zora Zhiruo Wang, Bhupalee Kalita, Runtian Gao, Aaron Ho, Andrew Oakleigh Nelson, Olexandr Isayev, Francisco Villaescusa-Navarro, Ching-Yao Lai, Howard Chen, Graham Neubig

EurekaBench tests whether AI agents can move beyond accurate prediction to uncover mechanisms and insights that genuinely advance scientific understanding.

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04Scientific Ai

HypoEvolve: Genetic Algorithms Enable Multi-Agent LLMs to Discover Scientific Hypotheses

Jieyuan Liu, Mengzhou Hu, Jefferson Chen, JungHo Kong, Pratibha Jagannatha, Yiming Gao, Dexter Pratt, Hsin-Yuan Lee, Zhiting Hu, Trey Ideker, Wei Wang, Eric P. Xing, Zhen Wang

HypoEvolve uses teams of LLM agents and genetic evolution to generate and refine drug-repurposing hypotheses that better align with biological evidence.

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05Scientific Ai

OpenAI4S: Code as Action, Science as Sessions

Gongbo Zhang, Hao Li, Yu Wang, Mujie Lin, Liuzhenghao Lv, Yicheng Mao, Yimi Wang, Jun Zhu, Minhan Tang, Zhengxiang Jiang, Yusong Wang, Jiayu Yao, Kunpeng Ning, Dawei Pang, Yonghong Tian, OpenAI4S Community, Yuyang Liu, Li Yuan

OpenAI4S turns AI-driven scientific research into inspectable, resumable sessions by treating executable code as the agent’s actions and tracking every computational step.

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06Scientific Ai

ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environments

Hejia Geng, Zesen Huang, Haoyang Li, Wenbin Li, Koutian Wu, Zihan Zhou, Yuanbo Pang, Weihao Liu, Zigong Xu, Zhiping Li, Zongzheng Zhang, Chuanfei Dong, Jiankai Sun, Tianzhe Zheng, Fengyu Xie, Yue Ma, Yueheng Shi, Tong Xie, Zonglin Di, Xianrong Liu, Qucheng Gao, Yimin Liu, Jiaming Pan, Sheng Huang, Xiao-Han Ma, Lanqing Yuan, Zhenlin Zhu, Ziang Liu, Ziyang Xu, Junkai Wang, Kangkai Liang, Jiayi Xian, Zehong Zhao, Liuwei Xu, Jingxu Xie, Peijin Zhang, Qiang Gao, Chengyi Xing, Zhe Zhao, Xi Wang, Yaopeng Xing, Xing Meng, Zhenfei Yin, Yingcheng Wu, Ling Yang

ScienceIDE turns scientific codebases into interactive training grounds where AI agents can learn to solve, verify, and improve scientific programming tasks.

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07Scientific Ai

Hakken: Predicting future discoveries to fill the gaps in today's knowledge

Tarek R. Besold, Uchenna Akujuobi, Pablo Sanchez, Alessandra Toniato, Kana Maruyama, Jihun Choi, Samy Badreddine, Frederick Gifford, Daniel Evans-Yamamoto, Sucheendra K. Palaniappan, Miquel Ferrer, Kae Nagano, Iris Rossell, Tom Joy, Hatem ElShazly, Chrysa Iliopoulou, Christoph Wehner, Thiviyan Thanapalasingam, Susana Nunes, Pedro G. Cotovio, Peter Wurman, Peter Stone, Hiroaki Kitano, Michael Spranger

Hakken uses evolving scientific knowledge graphs and language models to propose explainable future discoveries, including two newly validated biomedical interactions.

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10Scientific Ai

Planetary Prediction Engine: Autonomous Geospatial Prediction via Intelligent Data Selection and Foundation Model Embeddings

Evelyn Ma, Rama Kumar Pasumarthi, Kishwar Shafin, Mandar Sharma, Mimi Sun, Hamed Sadeghi, Dav M. Ebengo, Mbulayi Onesime, Rouslan Solomakhin, John Wamburu, William Ogallo, Aisha Walcott-Bryant, Sanxing Chen, Arbaaz Muslim, Yael Mayer, Ronald Ho, Roy Lee, Ruth Alcantara, Abdoulaye Diack, Monica Bharel, Lambert Rosique, Jeremy Amez-Droz, Christopher Haire, James Manyika, Yossi Matias, Niv Efron, Gautam Prasad, Shravya Shetty

PPE is an autonomous geospatial AI pipeline that finds the right data and models itself to make better predictions about health, disasters, food security, and disease spread.

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11Scientific Ai

Pushing the Limits of High-Resolution Weather Forecasting through Data Scaling

Yang Zhao, Peisong Niu, Tian Zhou, Ziqing Ma, Guanlong Ma, Rong Jin, Huiling Yuan, Liang Sun

By turning abundant coarse weather reanalysis into high-resolution training data, this work shows that better data scaling—not just better models—can substantially improve global forecasts.

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12Scientific Ai

WeatherNext 3: Increasing resolution and performance of global weather models with raw observations

Stephan Rasp, Boris Babenko, Dominic Masters, Andrew El-Kadi, Samier Merchant, Guy Shalev, Ilan Price, Fred Zyda, Remi Lam, Sasha Shysheya, Matthew Willson, Stratis Markou, Shreya Agrawal, Suhani Vora, Mohammed Alewi Hassen, Sunny Mak, Tom R. Andersson, Megan Bela, Akib Uddin, Nofar Peled Levi, Ben Gaiarin, Ferran Alet, Aaron Bell, Peter Battaglia, Alvaro Sanchez-Gonzalez

WeatherNext 3 brings AI weather forecasting closer to operational models by combining raw satellite and station observations with hourly, high-resolution global predictions.

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13Scientific Ai

Accelerating Scientific Research with Gemini in the Real-World

Samuel Schmidgall, Xiaokai Zhu, Marian Shaw, Lin Yang, Valentin Liévin, Jingyun Yang, Yuchen Zhuang, Tim Strother, Alex Bijamov, Min Woo Sun, Anil Palepu, Justin Chen, David Steiner, Jacqueline Shreibati, Wei-Hung Weng, Yilin Zhao, Xingjian Hu, Nicholas Zahn, Sadhya Garg, Julia Kirby, Yuxiang Gan, Jiaoli Li, Divy Thakkar, Shekoofeh Azizi, David Racz, Juraj Gottweis, Vivek Natarajan, Chenglin Wu, Tal Danino, Keran Rong, Haozhe Wang, Benoit Schillings, Yong Cheng, Quoc V. Le, Tao Tu

A Gemini-based multi-agent scientist conducts experiments, generates hypotheses, and writes research across multiple fields, suggesting a promising but not yet fully validated path toward automated scientific discovery.

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15Scientific Ai

LLMs Can Design Near-Optimal OR Algorithms

Jackie Baek

The study finds that frontier LLMs can independently produce near-competitive algorithms for important operations-research problems.

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16Scientific Ai

GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis

Alban Puech, Matteo Mazzonelli, Tamara R. Govindasamy, Mangaliso Mngomezulu, Héctor Maeso-García, Thomas Tolhurst, Javad Bayazi, Ali Moeini, Naomi Simumba, Celia Cintas, David Nelischer, Romeo Kienzler, Jonas Weiss, Anna Varbella, Florian Dörfler, Gabriela Hug, Martin Mevissen, Juan Bernabé-Moreno, François Mirallès, Hendrik F. Hamann, Etienne Vos, Thomas Brunschwiler

GENCO is a fast, unified neural solver and open framework for performing several demanding power-grid analyses while preserving physical feasibility.

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17Scientific Ai

Mechanist: AI as a Scientific Instrument for Discovering the Mechanisms of Intelligence

Mengru Wang, Junfeng Fang, Shuofei Qiao, Zhenqian Xu, Haoming Xu, Haoxiong Wang, Shumin Deng, Linyi Yang, Zhixiang Cui, Xin Xu, Yunzhi Yao, Buqiang Xu, Fei Shen, Haozhe Luo, Yunxiang Wei, Ningyu Zhang, Julian McAuley, Tat Seng Chua, Huajun Chen

Mechanist turns AI into an autonomous scientific instrument for discovering, testing, and controlling the mechanisms behind intelligent behavior.

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18Scientific Ai

OmniScientist: An Omni-Modal Omni-Discipline AI Scientist

Bobo Li, Hao Fei, Tianjie Ju, Mong-Li Lee, Wynne Hsu

OmniScientist is an AI researcher that observes raw scientific data across many modalities and disciplines, then turns those observations into experiments and complete research papers.

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20Scientific Ai

Resolving Structure in Prethermal Floquet Dynamics with Precision Quantum Computation

Eyal Leviatan, Tasneem Watad, Roy Perry, Lukas Broers, Mohammed Zuhair Mullath, Ori Alberton, Itai Arad, Yosi Atia, Eyal Bairey, Shaul Barkan, Matan Ben Dov, Asaf Berkovitch, Ewout van den Berg, Itsik Cohen, Omri Golan, Ilya Gurwich, Avieli Haber, Barak A. Katzir, Oded Kenneth, Roei Levi, Yotam Y. Lifshitz, Yaron Lukovsky, Ron Melcer, Adiel Meyer, Boris Muratov, Aviad Panahi, Gili Schul, Tali Shnaider, Maor Shutman, Alireza Seif, Tomonori Shirakawa, Asif Sinay, Vincent P. Su, Hayk Tepanyan, Omri Trebitch, Assaf Zubida, Dorit Aharonov, Hrant Gharibyan, Abhinav Kandala, Seiji Yunoki, Netanel H. Lindner

Researchers use precision error-mitigated quantum hardware to observe long-lived Floquet oscillations in a regime where classical simulations struggle.

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21Scientific Ai

Science Edge Evaluation: SEE the Missing Step Toward Real Scientific Discovery

Taolin Han, Yuchen Zhang, Jinghang Wang, Yun Wu, Wai Yuet Chiu, Zhaohai Li, Yifei Zhang, Jinxin Wang, Yuhao Zhou, Chen Zhao, Jiajia Li, Jiaxin Li, Qile Jin, Kewei Sun, Shuang Wu, Weiqi Zhai, Renquan Lv, Junchao Li, Ruodan Chen, Qingteng Chen, Zhibo Yang, Hu Wei, Lin Qu, Shuai Bai, Bing Zhao

SEE tests whether multimodal AI can move beyond explaining science to making reliable, evidence-grounded inferences from real experimental data.

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23Scientific Ai

EMBL AI Librarian: Life-Sciences Knowledge Layer for AI Agents

Luigi Sigillo, Matteo Silvestri, Francesco Tabaro, Rajat Bhatnagar, Syed Irtaza Mubashar, Matt Jeffryes, Daljit Nijjer, Vittorio Perera, Ola Spjuth, Julio Saez-Rodriguez, Melissa Harrison, Fabio Petroni

EMBL AI Librarian helps biology-focused AI agents turn natural-language questions into literature-backed answers and evidence.

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24Scientific Ai

When quantum thermal states look classical

Harald Putterman, Alexander Zlokapa, Jordan Cotler

The paper shows that surprisingly hot quantum systems can remain classically tractable long after entanglement and other quantum features begin to appear.

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28Scientific Ai

Multi-Turn Agentic Scientific Literature Search via Workflow Induction

Jisen Li, Bingxuan Li, Nanyi Jiang, Xuying Ning, Xiyao Wang, Yifan Shen, Heng Wang, Yuqing Jian, Xiaoxia Wu, Ben Athiwaratkun, Pan Lu, Jiaxuan You, Bingxin Zhao

PaperPilot turns scientific paper search into a controllable multi-turn workflow, making literature discovery more editable, inspectable, and effective.

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29Scientific Ai

S1-Omni: A Unified Multimodal Reasoning Model for Scientific Understanding, Prediction, and Generation

Jiahao Zhao, Junyi Liu, Lifeng Xu, Nan Xu, Qingli Wang, Qingxiao Li, Tianle Chen, Xiaoyu Wu, Yawen Zheng, Zikai Wang, Guanming Liu, Hequn Zhou, Jingyi Wang, Jingyuan Shu, Keqi Wang, Li He, Songyang Diao, Wenhui Xu, Xinyu Ren, Yaqin Fan, Yujin Zhou, Zhanao Yao

S1-Omni aims to be a general-purpose scientific AI model that understands and generates everything from molecules and proteins to spectra and scientific images.

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30Scientific Ai

TheoremGraph: Bridging Formal and Informal Mathematics

Simon Kurgan, Evan Wang, Eric Leonen, Sophie Szeto, Luke Alexander, Artemii Remizov, Jarod Alper, Giovanni Inchiostro, Vasily Ilin

This work builds a massive bridge between informal math papers and formal proof libraries, making the structure of mathematics searchable across both worlds.

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31Scientific Ai

Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning

Chen Tang, Yizhou Wang, Jianyu Wu, Lintao Wang, Shixiang Tang, Pengze Li, Encheng Su, Jun Yao, Jiabei Xiao, Yuqi Shi, Jielan Li, Hongxia Hao, Zhangyang Gao, Fang Wu, Ben Fei, Xiangyu Yue, Pan Tan, Bozitao Zhong, Jinouwen Zhang, Aoran Wang, Yan Lu, Jiaheng Liu, Xinzhu Ma, Liang Hong, Mingyue Zheng, Phil Torr, Bowen Zhou, Wanli Ouyang, Lei Bai

SciReasoner is a new scientific AI model that turns molecular and material structures into reasoning-friendly tokens to boost prediction accuracy and make its conclusions easier to interpret.

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32Scientific Ai

Autonomous Scientific Discovery via Iterative Meta-Reflection

Bingchen Zhao, Sara Beery, Oisin Mac Aodha

DiscoPER is an LLM-based system that autonomously explores data, tests hypotheses statistically, and reflects on its own findings to uncover new scientific patterns in multimodal datasets.

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33Scientific Ai

Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks

Young-Jun Lee, Seungone Kim, Minki Kang, Alistair Cheong Liang Chuen, Zerui Chen, Seungho Han, Taehee Jung, Dongyeop Kang

This paper teaches language models how to 'evolve' solutions across hundreds of optimization problems, turning search experience into reusable skill.

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34Scientific Ai

A Machine-Verified Proof of a Quantum-Optimization Conjecture

Uri Kol, Maor Ben-Shahar, Kfir Sulimany, Dirk Englund

An AI system helped discover and formally verify a new proof of a decade-old quantum optimization conjecture, showing how LLMs plus theorem provers can tackle open math and physics problems.

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35Scientific Ai

Deep Research in Physical Sciences: A Multi-Agent Framework and Comprehensive Benchmark

Yigeng Jiang, Tengchao Yang, Taoyong Cui, Jiaxing Wan, Yuan Wang, Weida Wang, Zhiyu Liu, Chuyi Peng, Binzhao Luo, Maoli Gao, Huaihai Huang, Yuqianer Zeng, Ziyang Zheng, Dongchen Huang, Chao Chen, Zichao Liu, Weiping Shen, Shuchen Pu, Siyu Zhou, Runmin Ma, Yusong Hu, Fei Chao, Bo Zhang, Xiawu Zheng, Zifu Wang, Lei Bai, Yunqi Cai, Shufei Zhang

This paper introduces a benchmark for AI scientific reasoning in physics and chemistry, along with a multi-agent system that improves performance and lowers inference cost on these hard research tasks.

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36Scientific Ai

How Post-Training Shapes Biological Reasoning Models

Lukas Fesser, Hanlin Zhang, Michelle M. Li, Eric Wang, Bryan Perozzi, Shekoofeh Azizi, Sham M. Kakade, Marinka Zitnik

This paper shows that in biological AI, more training is not always better: the way you mix pretraining, fine-tuning, and reinforcement learning strongly changes whether models generalize or overfit.

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37Scientific Ai

Towards Automating Scientific Review with Google's Paper Assistant Tool

Rajesh Jayaram, Drew Tyler, David Woodruff, Corinna Cortes, Yossi Matias, Vahab Mirrokni, Vincent Cohen-Addad

This paper introduces an AI assistant that helps review scientific papers by checking proofs, validating experiments, and finding errors before human referees do.

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39Scientific Ai

LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories

Baochang Ren, Xinjie Liu, Xi Chen, Yanshuo Liu, Chenxi Li, Daqi Gao, Zeqin Su, Jintao Xing, Zirui Xue, Rui Li, Xiangyu Zhao, Shuofei Qiao, Minting Pan, Wangmeng Zuo, Lei Bai, Dongzhan Zhou, Ningyu Zhang, Huajun Chen

LabVLA teaches vision-language-action models to execute scientific lab protocols with robot arms, aiming to make AI capable of carrying out experiments, not just planning them.

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40Scientific Ai

ResearchMath-14K: Scaling Research-Level Mathematics via Agents

Guijin Son, Seungyeop Yi, Minju Gwak, Hyunwoo Ko, Wongi Jang, Youngjae Yu

This paper builds the largest dataset so far for research-level math and shows that filtered AI-generated reasoning traces can help train stronger math models.

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41Scientific Ai

Formalizing Mathematics at Scale

Ahmad Rammal, Niket Patel, Fabian Gloeckle, Amaury Hayat, Julia Kempe, Remi Munos, Charles Arnal, Vivien Cabannes

This work turns thousands of AI agents loose on real math textbooks to build a massive, machine-checked Lean library, pushing automated formalization from a niche tool toward practical scale.

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42Scientific Ai

Skillful high-resolution weather forecasting independent of physical models

Pengcheng Zhao, Siqi Xiang, Weixin Jin, Zekun Ni, Jiang Bian, Zuliang Fang, Hongyu Sun, Bin Zhang, Richard E. Turner, Jonathan Weyn, Haiyu Dong, Kit Thambiratnam, Qi Zhang

ObsCast shows that high-resolution weather forecasts can be learned directly from observations, bypassing traditional numerical weather models while still delivering state-of-the-art regional predictions.

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43Scientific Ai

Learning Individual Dynamics from Sparse Cross-Sectional Snapshots

Christian Lagemann, Kai Lagemann, Steven L. Brunton, Sach Mukherjee

This paper shows how to infer personalized time evolution from sparse snapshot data, potentially replacing the need for dense longitudinal tracking in science and medicine.

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