机器学习是解决量子化学难题的必然选择，因传统方法已接近瓶颈。
Position: The Inevitable Transition to Machine Learning in Quantum Chemistry
- 将传统方法视为人工设计的机器学习，其潜力已被人类直觉耗尽。
- 现有方法在强关联问题上仍无突破，而机器学习可应对复杂性挑战。
- 适合关注量子化学前沿、算法创新的研究者参考。
求解量子多体问题在计算上属于难以处理（QMA-hard）的范畴。传统的电子近似方法——密度泛函理论（DFT）和波函数方法——虽不可或缺，但其发展已显饱和：DFT泛函数量激增却未收敛至精确泛函，强关联问题历经数十年仍未破解。本文认为，机器学习是未来最可行路径——非出于逻辑必然，而是基于决策论考量：无论问题本质是否真正困难，机器学习均能有效应对。我们重新将传统方法视为‘人工设计的机器学习’，其可探索的假设空间已耗尽于人类直觉。尽管挑战仍存，但研究方向清晰；相比之下，传统方法面临根本性障碍。因此，机器学习应被赋予量子化学下一阶段的战略优先地位。
原文摘要 · Abstract (English)
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.
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