发表机构
Institute of Chemistry, Academia Sinica, Taipei, Taiwan（中国科学院化学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
量子多体问题精确解计算难，传统方法发展遇瓶颈，机器学习被认为是量子化学最有前途的方向，它不受问题本质限制，近期传统方法可视为“手工制作机器学习”，虽有挑战但有路径，应获战略优先。
AI 中文摘要
寻找量子多体问题的精确解在计算上是难以处理的（QMA 难）。原子或分子中电子的传统近似方法——密度泛函理论和波函数方法——一直不可或缺，但它们的发展已显饱和迹象：DFT 泛函大量涌现却未趋向精确泛函，历经数十年努力强关联问题仍大多未解决。本文认为机器学习是最有前途的前进方向——并非基于逻辑必然性，而是基于决策理论：无论基础问题是真正困难还是仅缺乏简单解析解，机器学习都能成功。我们将近期传统方法的发展重新定义为已耗尽人类直觉可及假设空间的“手工制作机器学习”。虽仍有重大挑战，但有明确研究路径，不像传统方法面临的根本障碍。基于机器学习的方法在量子化学下一阶段值得战略优先考虑。
英文摘要
Finding exact solutions to the quantum many-body problem is computationally intractable (QMA-hard). Traditional approximations for electrons in an atom or molecule -- density functional theory and wavefunction methods -- have been indispensable, but their development shows signs of saturation: DFT functionals have proliferated without converging toward the exact functional, and strong correlation remains largely unsolved after decades of effort. This position paper argues that machine learning represents the most promising path forward -- not as a proof of logical necessity, but as a decision-theoretic argument: ML succeeds whether the underlying problems are truly hard or merely lack simple analytical solutions. We reframe recent traditional method development as ``hand-crafted machine learning'' that has exhausted the hypothesis space accessible to human intuition. Significant challenges remain, but these have clear research paths forward, unlike the fundamental barriers facing traditional approaches. ML-based approaches merit strategic priority in quantum chemistry's next phase.
CommentsAccepted as a position paper at ICML 2026. OpenReview forum: https://openreview.net/forum?id=Mq1oTEIwp4
Journal refProceedings of the 43rd International Conference on Machine Learning, Seoul, South Korea. PMLR 306, 2026
