Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
作者:Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, YuQing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik Bekkers, Michael Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, Shuiwang Ji
arXiv:2023/07/17
摘要: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 paper 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.
人工智能(AI)的进步正在推动自然科学发现的新范式。今天,人工智能已经开始通过改进、加速和使我们能够在广泛的空间和时间尺度上理解自然现象来推进自然科学,从而产生了一个被称为人工智能科学(AI4Science)的新研究领域。作为一种新兴的研究范式,AI4科学的独特之处在于它是一个巨大的、高度跨学科的领域。因此,需要对这一领域进行统一的技术处理,但这具有挑战性。本文旨在对AI4科学的一个子领域进行技术上的全面介绍;即用于量子、原子和连续体系统的人工智能。这些领域旨在了解从亚原子(波函数和电子密度)、原子(分子、蛋白质、材料和相互作用)到宏观(流体、气候和地下)尺度的物理世界,并形成AI4科学的一个重要子区域。专注于这些领域的一个独特优势是,它们在很大程度上共享一系列共同的挑战,从而实现统一和基础的治疗。一个关键的共同挑战是如何通过深度学习方法捕捉自然系统中的物理第一原理,尤其是对称性。我们对实现等变对称变换的技术进行了深入而直观的描述。我们还讨论了其他常见的技术挑战,包括可解释性、分布外泛化、基于基础和大型语言模型的知识转移以及不确定性量化。为了促进学习和教育,我们提供了我们认为有用的资源分类列表。我们努力做到彻底和统一,并希望这一初步努力能够引发更多的社区利益和努力,进一步推进AI4Science。