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RESEARCH PAPER · Science Advances · 2025

Negative chemical data boosts language models in reaction outcome prediction

Alessandra Toniato, Alain C. Vaucher, Teodoro Laino, Mara Graziani

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Negative chemical data boosts language models in reaction outcome predictionOriginalA FAIR research data infrastructure for high-throughput digital chemistry4The Problem of Negative Results in Materials AI: A Conceptual Proposal for Failure-Aware Learning Systems0Optimizing Model Learning Performance on a Challenging Heck Reaction Yield Data Set2Automating Deep Learning-Based Generation and Evaluation of De Novo Chemical Reaction with ChemRxnSAGE1Prediction and Analysis of Chemical Molecular Reaction Paths Driven by Deep Learning Algorithms0A comprehensive survey of AI-based retrosynthesis planning: Datasets, models, and tools3Machine learning guided O 2 reduction to H 2 O 2 on BN co-doped γ-graphyne: insights from time-domain atomistic dynamics0Natural-Product Models Learn What Databases Choose to Record: A Data-Governance Architecture for Structures, Spectra, Bioassays, Taxonomy, Provenance, and Negative Results0Perspective: Why Materials AI Needs More Failure Reporting — A Position on Negative Results as Community Infrastructure0Physics-interpreted closed-loop optimization and interpretable process insight in perovskite photovoltaics0Protocol-guided evaluation of reaction yield prediction under predictable distribution shifts: a review0Reaction Prediction Can Change How Natural Product Scaffolds Are Optimized by Connecting Biosynthetic Logic, Semisynthesis, and Medicinal Chemistry Search0Roadmap for transforming heterogeneous catalysis with artificial intelligence27Recent advances in data‐driven and artificial intelligence‐integrated perovskite solar cells: From design to self‐driving laboratories7Reimagining metal-organic framework discovery: Integrating experiment, computation, and artificial intelligence6Data Distribution Matters: Enhancing model generalization in Data-Driven Materials Discovery by intentionally exploiting both positive and negative data1Microfluidics: A Scalable Solution to Data Challenges in Chemical AI0Data‐Driven Approach to Understanding Tetrabutylammonium Decatungstate‐Catalyzed C(sp 3 )–H Functionalization Selectivity0Data‐Driven Approach to Understanding Tetrabutylammonium Decatungstate‐Catalyzed C(sp 3 )–H Functionalization Selectivity1From chemical space to synthetic reality: A framework for makeable-by-design generative AI in drug discovery0Datasets for AI‐Driven Energetic Materials Research: Construction, Progress, and Challenges0

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  1. A FAIR research data infrastructure for high-throughput digital chemistry

    Digital Discovery · 2025-01-01

    Alice Gauthier, Laure Vancauwenberghe, Jean-Charles Cousty, Cyril Matthey-Doret, Robin M. Franken, Sabine Maennel, Pascal Miéville, Oksana Riba‐Grognuz

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  2. The Problem of Negative Results in Materials AI: A Conceptual Proposal for Failure-Aware Learning Systems

    Journal of Artificial Intelligence for Materials Science · 2025-01-01

    Fatima Zahra Amrani, Youssef Benali

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  3. Optimizing Model Learning Performance on a Challenging Heck Reaction Yield Data Set

    The Journal of Organic Chemistry · 2025-09-03

    Shen Wang, Yining Liu, Weiren Zhao, Yang Li

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  4. Automating Deep Learning-Based Generation and Evaluation of De Novo Chemical Reaction with ChemRxnSAGE

    Journal of Chemical Information and Modeling · 2025-10-17

    Anis Ismail, Joe Tekli, Brigitte Wex

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  5. Prediction and Analysis of Chemical Molecular Reaction Paths Driven by Deep Learning Algorithms

    IEEE International Conference on Electrical, Automation and Computer Engineering (ICEACE) · 2025-12-26

    Yunlai Cai

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  6. A comprehensive survey of AI-based retrosynthesis planning: Datasets, models, and tools

    The Innovation Informatics · 2026-01-01

    Peng-Cheng Zhao, Hong-Ze Zhou, Qiong Wang, Zhen-Yi Wu, Hui Yu, J. W. Shi

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  7. Machine learning guided O 2 reduction to H 2 O 2 on BN co-doped γ-graphyne: insights from time-domain atomistic dynamics

    Nanoscale · 2026-01-01

    Subhash Kumar, Debasis Maji, Debaditya Barman, Pranab Sarkar

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  8. Natural-Product Models Learn What Databases Choose to Record: A Data-Governance Architecture for Structures, Spectra, Bioassays, Taxonomy, Provenance, and Negative Results

    Specialty journal of Pharmacognosy Phytochemistry and Biotechnology · 2026-01-01

    Gabriel Costa, Lucas Ribeiro, Ricardo Alves, Mariana Lopes

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  9. Perspective: Why Materials AI Needs More Failure Reporting — A Position on Negative Results as Community Infrastructure

    Journal of Computational and Data-Driven Materials Engineering · 2026-01-01

    Anna Novak, Tomáš Hrubý

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  10. Physics-interpreted closed-loop optimization and interpretable process insight in perovskite photovoltaics

    Energy & Environmental Science · 2026-01-01

    Ziyu Xiong, Wei Meng, Tengteng Yang, Junyan Zhu, Zhangyu Yuan, Ying Shang, Jialin Wu, Jiahao Li, Zhuo Feng, Lin Mao, Kang An, Ning Li

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  11. Protocol-guided evaluation of reaction yield prediction under predictable distribution shifts: a review

    Reaction Chemistry & Engineering · 2026-01-01

    Zhengtong Qiu, Haoyu Yuan, Ao Chen, Chaonan Mu

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  12. Reaction Prediction Can Change How Natural Product Scaffolds Are Optimized by Connecting Biosynthetic Logic, Semisynthesis, and Medicinal Chemistry Search

    International Journal of Pharmaceutical and Phytopharmacological Research · 2026-01-01

    Andrei Popescu, Mihai Ionescu, Elena Stan, Cristina Radu

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  13. Roadmap for transforming heterogeneous catalysis with artificial intelligence

    Nature Catalysis · 2026-02-16

    Hongliang Xin, John Kitchin, Núria López, Neil M. Schweitzer, Nongnuch Artrith, Fanglin Che, Lars Christian Grabow, G. T. Kasun Kalhara Gunasooriya, Heather J. Kulik, Teodoro Laino, H. C. Li, Suljo Linic, Andrew James Medford, Randall J. Meyer, Jiayu Peng, Cory Phillips, Jin Qian, Long Hao Qi, Wendy J. Shaw, Zachary W. Ulissi, Siwen Wang, Xiaonan Wang

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  14. Recent advances in data‐driven and artificial intelligence‐integrated perovskite solar cells: From design to self‐driving laboratories

    InfoMat · 2026-02-20

    Jaehee Lee, Juhwan Noh, Hyunjun Lee, Yea‐Lee Lee, Jino Im, Jangwon Seo

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  15. Reimagining metal-organic framework discovery: Integrating experiment, computation, and artificial intelligence

    Chem · 2026-02-25

    Madeleine A. Gaidimas, Jiaru Bai, Yeonghun Kang, Kent O. Kirlikovali, Varinia Bernales, Alán Aspuru‐Guzik, Omar K. Farha

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  16. Data Distribution Matters: Enhancing model generalization in Data-Driven Materials Discovery by intentionally exploiting both positive and negative data

    Acta Physico-Chimica Sinica · 2026-03-01

    Yiheng Wu, Lijun Liu, Wei Deng, Haodong Jiang, Ruotong Li, Ming Hu, Bo Peng, Dapeng Wang

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  17. Microfluidics: A Scalable Solution to Data Challenges in Chemical AI

    Synlett · 2026-05-06

    Rico Warias, Detlev Belder, Julia Westermayr, Tobias A. M. Gulder, Tanja Gulder

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  18. Data‐Driven Approach to Understanding Tetrabutylammonium Decatungstate‐Catalyzed C(sp 3 )–H Functionalization Selectivity

    Angewandte Chemie · 2026-05-22

    Mimi Lavin, Makeda A. Tekle‐Smith

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  19. Data‐Driven Approach to Understanding Tetrabutylammonium Decatungstate‐Catalyzed C(sp 3 )–H Functionalization Selectivity

    Angewandte Chemie International Edition · 2026-05-22

    Mimi Lavin, Makeda A. Tekle‐Smith

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  20. From chemical space to synthetic reality: A framework for makeable-by-design generative AI in drug discovery

    Drug Discovery Today · 2026-09-03

    Sebastián O. Simonetti, Aldana B. Moroni, María Marta Zanardi, Frank Kozielski, Geoffrey Wells, Exequiel O. J. Porta

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  21. Datasets for AI‐Driven Energetic Materials Research: Construction, Progress, and Challenges

    Propellants Explosives Pyrotechnics · 2026-10-09

    Weilin Wang, Hongwei Gao, Peng Zhu, Zhixiang Qian, Aijia Liu, Yuyao Zhang, Yinghua Ye, Ruiqi Shen

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