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Complex chemical space and limited knowledge scope with biases holds immense challenge for human scientists, yet in automated materials discovery.
A survey of monte carlo tree search methods
Cameron B Browne, Edward Powley, Daniel Whitehouse, Simon M Lucas, Peter I Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis, and Simon Colton · 2012
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Kirkeby Fidjeland, Georg Ostrovski, Stig Petersen, Charlie Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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Accelerated search for materials with targeted properties by adaptive design
Dezhen Xue, Prasanna V. Balachandran, John Hogden, James Theiler, Deqing Xue, and Turab Lookman · 2016
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A tutorial on bayesian optimization
P. Frazier · 2018
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Transparent conducting materials discovery using high-throughput computing
Guillaume Brunin, Francesco Ricci, Viet-Anh Ha, Gian‐Marco Rignanese, and Geoffroy Hautier · 2019
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Crystal: a multi-agent ai system for automated mapping of materials’ crystal structures
Carla Pedro Gomes, Junwen Bai, Yexiang Xue, Johan Bjorck, Brendan H. Rappazzo, Sebastian Ament, Richard Bernstein, Shufeng Kong, Santosh K. Suram, Robert Bruce van Dover, and J. Gregoire · 2019
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Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design
Turab Lookman, Prasanna V. Balachandran, Dezhen Xue, and Ruihao Yuan · 2019
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Toward “on‐demand” materials synthesis and scientific discovery through intelligent robots
Jiagen Li, Yuxiao Tu, Rulin Liu, Yihua Lu, and Xi Zhu · 2020
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Data‐driven materials innovation and applications
Zhuo Wang, Zhehao Sun, Hang Yin, Xinghui Liu, Jinlan Wang, Haitao Zhao, Cheng Heng Pang, Tao Wu, Shuzhou Li, Zongyou Yin, and Xue‐Feng Yu · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed H. Chi, F. Xia, Quoc Le, and Denny Zhou · 2022
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Augmenting large language models with chemistry tools
Andrés M Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D. White, and Philippe Schwaller · 2023
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Targeted materials discovery using bayesian algorithm execution
Sathya R. Chitturi, Akash Ramdas, Yue Wu, Brian Rohr, Stefano Ermon, Jennifer Dionne, Felipe H. da Jornada, Mike Dunne, Christopher Tassone, Willie Neiswanger, and Daniel Ratner · 2023
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Application of machine learning in material synthesis and property prediction
Guannan Huang, Yan Guo, Ye Chen, and Zhengwei Nie · 2023
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Materials discovery with extreme properties via reinforcement learning-guided combinatorial chemistry
Hyunseung Kim, Haeyeon Choi, Dongju Kang, Won Bo Lee, and Jonggeol Na · 2023
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Darwin series: Domain specific large language models for natural science
Tong Xie, Yuwei Wan, Wei Huang, Zhenyu Yin, Yixuan Liu, Shaozhou Wang, Qingyuan Linghu, Chunyu Kit, Clara Grazian, Wenjie Zhang, Imran Razzak, and Bram Hoex · 2023
Cited alongside, same era.
Large language models for automated open-domain scientific hypotheses discovery
Zonglin Yang, Xinya Du, Junxian Li, Jie Zheng, Soujanya Poria, and E. Cambria · 2023
Matpilot: an llm-enabled ai materials scientist under the framework of human-machine collaboration
Ziqi Ni, Yahao Li, Kaijia Hu, Kunyuan Han, Ming Xu, Xingyu Chen, Fengqi Liu, Yicong Ye, and Shuxin Bai · 2024
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Leveraging large language models for explaining material synthesis mechanisms: The foundation of materials discovery
Yingming Pu, Liping Huang, Tao Lin, and Hongyu Chen · 2024
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A review of large language models and autonomous agents in chemistry
Mayk Caldas Ramos, Christopher J. Collison, and Andrew D. White · 2024
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Haoyang Su, Renqi Chen, Shixiang Tang, Xinzhe Zheng, Jingzhe Li, Zhenfei Yin, Wanli Ouyang, and Nanqing Dong · 2024
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A survey on large language model based autonomous agents
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Cited alongside, same era.
Jinheon Baek, Sujay Kumar Jauhar, Silviu Cucerzan, and Sung Ju Hwang · 2024
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Chemgymrl: A customizable interactive framework for reinforcement learning for digital chemistry
Chris Beeler, Sriram Ganapathi Subramanian, Kyle Sprague, Mark Baula, Nouha Chatti, Amanuel Dawit, Xinkai Li, Nicholas Paquin, Mitchell Shahen, Zihan Yang, Colin Bellinger, Mark Crowley, and Isaac Tamblyn · 2024
Cited alongside, same era.
Large language models for causal hypothesis generation in science
Kai-Hendrik Cohrs, Emiliano Díaz, Vasileios Sitokonstantinou, Gherardo Varando, and Gustau Camps-Valls · 2024
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Mandrel: Modular reinforcement learning pipelines for material discovery
Clyde Fare, George K. Holt, Lamogha Chiazor, Michail Smyrnakis, Robert Tracey, and Lan Hoang · 2024
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From generalist to specialist: A survey of large language models for chemistry
Yang Han, Ziping Wan, Lu Chen, Kai Yu, and Xin Chen · 2024
Cited alongside, same era.
The ai scientist: Towards fully automated open-ended scientific discovery
Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob N. Foerster, Jeff Clune, and David Ha · 2024
Cited alongside, same era.
Protagents: protein discovery via large language model multi-agent collaborations combining physics and machine learning
Alireza Ghafarollahi and Markus J. Buehler
Cited in the paper.
Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, et al · 2024
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Moose-chem: Large language models for rediscovering unseen chemistry scientific hypotheses
Zonglin Yang, Wanhao Liu, Ben Gao, Tong Xie, Yuqiang Li, Wanli Ouyang, Soujanya Poria, Erik Cambria, and Dongzhan Zhou · 2024
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Hypothesis generation with large language models
Yangqiaoyu Zhou, Haokun Liu, Tejes Srivastava, Hongyuan Mei, and Chenhao Tan · 2024
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Hypothesis generation for materials discovery and design using goal-driven and constraint-guided llm agents
Shrinidhi Kumbhar, Venkatesh Mishra, Kevin Coutinho, Divij Handa, Ashif Iquebal, and Chitta Baral · 2025
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Llm4sr: A survey on large language models for scientific research
Ziming Luo, Zonglin Yang, Zexin Xu, Wei Yang, and Xinya Du · 2025
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Agent laboratory: Using llm agents as research assistants
Samuel Schmidgall, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu, Zicheng Liu, and Emad Barsoum · 2025
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