Advanced Quantum Chemistry: Some thoughts about DFT and machine learning

Karen Sargsyan, Chao-Ping Hsu

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.

Pavlo O. Dral

As the quantum chemistry (QC) community embraces machine learning (ML), the number of new methods and applications based on the combination of QC and ML is surging. In this Perspective, a view of the current state of affairs in this new and exciting research field is offered, challenges of using machine learning in quantum chemistry applications are described, and potential future developments are outlined. Specifically, examples of how machine learning is used to improve the accuracy and accelerate quantum chemical research are shown. Generalization and classification of existing techniques are provided to ease the navigation in the sea of literature and to guide researchers entering the field. The emphasis of this Perspective is on supervised machine learning.

Bing Huang, Nadine O. Symonds, O. Anatole von Lilienfeld

Bing Huang, Nadine O. Symonds, O. Anatole von Lilienfeld

Kanchu Srilatha

Quantum chemistry has experienced transformative advancements from 2010 to 2024, propelled by developments in quantum computing, machine learning, and computational methodologies. These innovations have enabled precise simulations of complex molecular systems, fostering significant progress in materials science, drug discovery, and energy research. The integration of quantum computing has introduced algorithms like the Variational Quantum Eigensolver (VQE), facilitating the calculation of molecular ground-state energies. Machine learning techniques have enhanced predictive modeling, optimizing chemical reactions and material properties. Additionally, the development of user-friendly quantum chemistry software has democratized access to sophisticated computational tools. Despite these advancements, challenges such as scalability, error mitigation in quantum computations, and the need for high-quality data persist. Addressing these issues is crucial for the continued evolution of quantum chemistry, promising transformative impacts on technology and society.