Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Abstract
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science.
Cite
Text
Zhang et al. "Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems." Foundations and Trends in Machine Learning, 2025. doi:10.1561/2200000115Markdown
[Zhang et al. "Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems." Foundations and Trends in Machine Learning, 2025.](https://mlanthology.org/ftml/2025/zhang2025ftml-artificial/) doi:10.1561/2200000115BibTeX
@article{zhang2025ftml-artificial,
title = {{Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems}},
author = {Zhang, Xuan and Wang, Limei and Helwig, Jacob and Luo, Youzhi and Fu, Cong and Xie, Yaochen and Liu, Meng and Lin, Yuchao and Xu, Zhao and Yan, Keqiang and Adams, Keir and Weiler, Maurice and Li, Xiner and Fu, Tianfan and Wang, Yucheng and Strasser, Alex and Yu, Haiyang and Xie, Yuqing and Fu, Xiang and Xu, Shenglong and Liu, Yi and Du, Yuanqi and Saxton, Alexandra and Ling, Hongyi and Lawrence, Hannah and Stärk, Hannes and Gui, Shurui and Edwards, Carl and Gao, Nicholas and Ladera, Adriana and Wu, Tailin and Hofgard, Elyssa F. and Tehrani, Aria Mansouri and Wang, Rui and Daigavane, Ameya and Bohde, Montgomery and Kurtin, Jerry and Huang, Qian and Phung, Tuong and Xu, Minkai and Joshi, Chaitanya K. and Mathis, Simon V. and Azizzadenesheli, Kamyar and Fang, Ada and Aspuru-Guzik, Alán and Bekkers, Erik J. and Bronstein, Michael M. and Zitnik, Marinka and Anandkumar, Anima and Ermon, Stefano and Liò, Pietro and Yu, Rose and Günnemann, Stephan and Leskovec, Jure and Ji, Heng and Sun, Jimeng and Barzilay, Regina and Jaakkola, Tommi S. and Coley, Connor W. and Qian, Xiaoning and Qian, Xiaofeng and Smidt, Tess E. and Ji, Shuiwang},
journal = {Foundations and Trends in Machine Learning},
year = {2025},
pages = {385-912},
doi = {10.1561/2200000115},
volume = {18},
url = {https://mlanthology.org/ftml/2025/zhang2025ftml-artificial/}
}