Staff View: :: Library Catalog

Saved in:

Bibliographic Details
Main Authors:	Dale, Stephen G., Kazeev, Nikita, Price, Alastair J. A., Posligua, Victor, Roche, Stephan, von Lilienfeld, O. Anatole, Novoselov, Konstantin S., Bresson, Xavier, Mengaldo, Gianmarco, Chen, Xudong, O'Kane, Terence J., Lines, Emily R., Allen, Matthew J., Debus, Amandine E., Miller, Clayton, Zhou, Jiayu, Dodge, Hiroko H., Rousseau, David, Ustyuzhanin, Andrey, Yan, Ziyun, Lanza, Mario, Sciarrino, Fabio, Yoshida, Ryo, Leong, Zhidong, Tan, Teck Leong, Li, Qianxiao, Kabylda, Adil, Poltavsky, Igor, Tkatchenko, Alexandre, Tawfik, Sherif Abdulkader, Kamath, Prathami Divakar, Inizan, Theo Jaffrelot, Persson, Kristin A., Li, Bryant Y., Karan, Vir, Duan, Chenru, Jia, Haojun, Zhao, Qiyuan, Hayashi, Hiroyuki, Seko, Atsuto, Tanaka, Isao, Yaghi, Omar M., Gould, Tim, Chan, Bun, Vuckovic, Stefan, Li, Tianbo, Lin, Min, Tang, Zehcen, Li, Yang, Xu, Yong, Joshi, Amrita, Wang, Xiaonan, Ng, Leonard W. T., Kalinin, Sergei V., Ahmadi, Mahshid, Zhang, Jiyizhe, Zhang, Shuyuan, Lapkin, Alexei, Xiao, Ming, Wu, Zhe, Hippalgaonkar, Kedar, Wong, Limsoon, Bastonero, Lorenzo, Marzari, Nicola, Cordoba, Dorye Luis Esteras, Tomut, Andrei, Andrade, Alba Quinones, Garcia, Jose-Hugo
Format:	Preprint
Published:	2025
Subjects:	Physics and Society Artificial Intelligence Atmospheric and Oceanic Physics Atomic and Molecular Clusters Chemical Physics Computational Physics
Online Access:	https://arxiv.org/abs/2511.20976
Tags:	Add Tag No Tags, Be the first to tag this record!

_version_	1866914171351728128
author	Dale, Stephen G. Kazeev, Nikita Price, Alastair J. A. Posligua, Victor Roche, Stephan von Lilienfeld, O. Anatole Novoselov, Konstantin S. Bresson, Xavier Mengaldo, Gianmarco Chen, Xudong O'Kane, Terence J. Lines, Emily R. Allen, Matthew J. Debus, Amandine E. Miller, Clayton Zhou, Jiayu Dodge, Hiroko H. Rousseau, David Ustyuzhanin, Andrey Yan, Ziyun Lanza, Mario Sciarrino, Fabio Yoshida, Ryo Leong, Zhidong Tan, Teck Leong Li, Qianxiao Kabylda, Adil Poltavsky, Igor Tkatchenko, Alexandre Tawfik, Sherif Abdulkader Kamath, Prathami Divakar Inizan, Theo Jaffrelot Persson, Kristin A. Li, Bryant Y. Karan, Vir Duan, Chenru Jia, Haojun Zhao, Qiyuan Hayashi, Hiroyuki Seko, Atsuto Tanaka, Isao Yaghi, Omar M. Gould, Tim Chan, Bun Vuckovic, Stefan Li, Tianbo Lin, Min Tang, Zehcen Li, Yang Xu, Yong Joshi, Amrita Wang, Xiaonan Ng, Leonard W. T. Kalinin, Sergei V. Ahmadi, Mahshid Zhang, Jiyizhe Zhang, Shuyuan Lapkin, Alexei Xiao, Ming Wu, Zhe Hippalgaonkar, Kedar Wong, Limsoon Bastonero, Lorenzo Marzari, Nicola Cordoba, Dorye Luis Esteras Tomut, Andrei Andrade, Alba Quinones Garcia, Jose-Hugo
author_facet	Dale, Stephen G. Kazeev, Nikita Price, Alastair J. A. Posligua, Victor Roche, Stephan von Lilienfeld, O. Anatole Novoselov, Konstantin S. Bresson, Xavier Mengaldo, Gianmarco Chen, Xudong O'Kane, Terence J. Lines, Emily R. Allen, Matthew J. Debus, Amandine E. Miller, Clayton Zhou, Jiayu Dodge, Hiroko H. Rousseau, David Ustyuzhanin, Andrey Yan, Ziyun Lanza, Mario Sciarrino, Fabio Yoshida, Ryo Leong, Zhidong Tan, Teck Leong Li, Qianxiao Kabylda, Adil Poltavsky, Igor Tkatchenko, Alexandre Tawfik, Sherif Abdulkader Kamath, Prathami Divakar Inizan, Theo Jaffrelot Persson, Kristin A. Li, Bryant Y. Karan, Vir Duan, Chenru Jia, Haojun Zhao, Qiyuan Hayashi, Hiroyuki Seko, Atsuto Tanaka, Isao Yaghi, Omar M. Gould, Tim Chan, Bun Vuckovic, Stefan Li, Tianbo Lin, Min Tang, Zehcen Li, Yang Xu, Yong Joshi, Amrita Wang, Xiaonan Ng, Leonard W. T. Kalinin, Sergei V. Ahmadi, Mahshid Zhang, Jiyizhe Zhang, Shuyuan Lapkin, Alexei Xiao, Ming Wu, Zhe Hippalgaonkar, Kedar Wong, Limsoon Bastonero, Lorenzo Marzari, Nicola Cordoba, Dorye Luis Esteras Tomut, Andrei Andrade, Alba Quinones Garcia, Jose-Hugo
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2511_20976
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions Dale, Stephen G. Kazeev, Nikita Price, Alastair J. A. Posligua, Victor Roche, Stephan von Lilienfeld, O. Anatole Novoselov, Konstantin S. Bresson, Xavier Mengaldo, Gianmarco Chen, Xudong O'Kane, Terence J. Lines, Emily R. Allen, Matthew J. Debus, Amandine E. Miller, Clayton Zhou, Jiayu Dodge, Hiroko H. Rousseau, David Ustyuzhanin, Andrey Yan, Ziyun Lanza, Mario Sciarrino, Fabio Yoshida, Ryo Leong, Zhidong Tan, Teck Leong Li, Qianxiao Kabylda, Adil Poltavsky, Igor Tkatchenko, Alexandre Tawfik, Sherif Abdulkader Kamath, Prathami Divakar Inizan, Theo Jaffrelot Persson, Kristin A. Li, Bryant Y. Karan, Vir Duan, Chenru Jia, Haojun Zhao, Qiyuan Hayashi, Hiroyuki Seko, Atsuto Tanaka, Isao Yaghi, Omar M. Gould, Tim Chan, Bun Vuckovic, Stefan Li, Tianbo Lin, Min Tang, Zehcen Li, Yang Xu, Yong Joshi, Amrita Wang, Xiaonan Ng, Leonard W. T. Kalinin, Sergei V. Ahmadi, Mahshid Zhang, Jiyizhe Zhang, Shuyuan Lapkin, Alexei Xiao, Ming Wu, Zhe Hippalgaonkar, Kedar Wong, Limsoon Bastonero, Lorenzo Marzari, Nicola Cordoba, Dorye Luis Esteras Tomut, Andrei Andrade, Alba Quinones Garcia, Jose-Hugo Physics and Society Artificial Intelligence Atmospheric and Oceanic Physics Atomic and Molecular Clusters Chemical Physics Computational Physics 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.
title	AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
topic	Physics and Society Artificial Intelligence Atmospheric and Oceanic Physics Atomic and Molecular Clusters Chemical Physics Computational Physics
url	https://arxiv.org/abs/2511.20976

Similar Items