AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions

• URL: http://arxiv.org/abs/2511.20976v1
• Date: Wed, 26 Nov 2025 02:10:28 GMT
• Title: AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
• Authors: Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price, Victor Posligua, Stephan Roche, O. Anatole von Lilienfeld, Konstantin S. Novoselov, Xavier Bresson, Gianmarco Mengaldo, Xudong Chen, Terence J. O'Kane, Emily R. Lines, Matthew J. Allen, Amandine E. Debus, Clayton Miller, Jiayu Zhou, Hiroko H. Dodge, David Rousseau, Andrey Ustyuzhanin, Ziyun Yan, Mario Lanza, Fabio Sciarrino, Ryo Yoshida, Zhidong Leong, Teck Leong Tan, Qianxiao Li, Adil Kabylda, Igor Poltavsky, Alexandre Tkatchenko, Sherif Abdulkader Tawfik, Prathami Divakar Kamath, Theo Jaffrelot Inizan, Kristin A. Persson, Bryant Y. Li, Vir Karan, Chenru Duan, Haojun Jia, Qiyuan Zhao, Hiroyuki Hayashi, Atsuto Seko, Isao Tanaka, Omar M. Yaghi, Tim Gould, Bun Chan, Stefan Vuckovic, Tianbo Li, Min Lin, Zehcen Tang, Yang Li, Yong Xu, Amrita Joshi, Xiaonan Wang, Leonard W. T. Ng, Sergei V. Kalinin, Mahshid Ahmadi, Jiyizhe Zhang, Shuyuan Zhang, Alexei Lapkin, Ming Xiao, Zhe Wu, Kedar Hippalgaonkar, Limsoon Wong, Lorenzo Bastonero, Nicola Marzari, Dorye Luis Esteras Cordoba, Andrei Tomut, Alba Quinones Andrade, Jose-Hugo Garcia,
• Abstract summary: We look at AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing.<n>Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific synthesis.<n>Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation.
• Score: 65.44445343399126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, predict, and design. In this roadmap we provide a forward-looking view of AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing. Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific workflows that connect simulations to experiments and generative systems grounded in synthesisability rather than purely idealised phases. Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation while maintaining reproducibility and physical interpretability. Taken together, these perspectives outline where AI-enabled science stands today, identify bottlenecks in data, methods and infrastructure, and chart concrete directions for building AI systems that are not only more powerful but also more transparent and capable of accelerating discovery in complex real-world environments.

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