Position: The Inevitable Transition to Machine Learning in Quantum Chemistry

arXiv:2607.18281v1 Announce Type: cross Abstract: 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.

Abstract: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.

Accepted as a position paper at ICML 2026. OpenReview forum: this https URL

Chemical Physics (physics.chem-ph); Machine Learning (cs.LG); Quantum Physics (quant-ph)

arXiv:2607.18281 [physics.chem-ph]

(or arXiv:2607.18281v1 [physics.chem-ph] for this version)

https://doi.org/10.48550/arXiv.2607.18281 Focus to learn more arXiv-issued DOI via DataCite

Proceedings of the 43rd International Conference on Machine Learning, Seoul, South Korea. PMLR 306, 2026

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Position: The Inevitable Transition to Machine Learning in Quantum Chemistry

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