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 St?rk, 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）的進(jìn)步正在推動自然科學(xué)發(fā)現(xiàn)的新范式。今天，人工智能已經(jīng)開始通過改進(jìn)、加速和使我們能夠在廣泛的空間和時間尺度上理解自然現(xiàn)象來推進(jìn)自然科學(xué)，從而產(chǎn)生了一個被稱為人工智能科學(xué)（AI4Science）的新研究領(lǐng)域。作為一種新興的研究范式，AI4科學(xué)的獨(dú)特之處在于它是一個巨大的、高度跨學(xué)科的領(lǐng)域。因此，需要對這一領(lǐng)域進(jìn)行統(tǒng)一的技術(shù)處理，但這具有挑戰(zhàn)性。本文旨在對AI4科學(xué)的一個子領(lǐng)域進(jìn)行技術(shù)上的全面介紹；即用于量子、原子和連續(xù)體系統(tǒng)的人工智能。這些領(lǐng)域旨在了解從亞原子（波函數(shù)和電子密度）、原子（分子、蛋白質(zhì)、材料和相互作用）到宏觀（流體、氣候和地下）尺度的物理世界，并形成AI4科學(xué)的一個重要子區(qū)域。專注于這些領(lǐng)域的一個獨(dú)特優(yōu)勢是，它們在很大程度上共享一系列共同的挑戰(zhàn)，從而實(shí)現(xiàn)統(tǒng)一和基礎(chǔ)的治療。一個關(guān)鍵的共同挑戰(zhàn)是如何通過深度學(xué)習(xí)方法捕捉自然系統(tǒng)中的物理第一原理，尤其是對稱性。我們對實(shí)現(xiàn)等變對稱變換的技術(shù)進(jìn)行了深入而直觀的描述。我們還討論了其他常見的技術(shù)挑戰(zhàn)，包括可解釋性、分布外泛化、基于基礎(chǔ)和大型語言模型的知識轉(zhuǎn)移以及不確定性量化。為了促進(jìn)學(xué)習(xí)和教育，我們提供了我們認(rèn)為有用的資源分類列表。我們努力做到徹底和統(tǒng)一，并希望這一初步努力能夠引發(fā)更多的社區(qū)利益和努力，進(jìn)一步推進(jìn)AI4Science。