Lang Lucas, Cezar Henrique M, Adamowicz Ludwik, Pedersen Thomas B
Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033 Blindern, 0315 Oslo, Norway.
Technische Universität Berlin, Institut für Chemie, Theoretische Chemie/Quantenchemie, Sekr. C7, Straße des 17. Juni 135, 10623 Berlin, Germany.
J Am Chem Soc. 2024 Jan 24;146(3):1760-1764. doi: 10.1021/jacs.3c11467. Epub 2024 Jan 10.
Molecular structure, a key concept of chemistry, has remained elusive from the perspective of all-particle quantum mechanics, despite many efforts. Viewing molecular structure as a manifestation of strong statistical correlation between nuclear positions, we propose a practical method based on Markov chain Monte Carlo sampling and unsupervised machine learning. Application to the D molecule unambiguously shows that it possesses an equilateral triangular structure. These results provide a major step forward in our understanding of the molecular structure from fundamental quantum principles.
分子结构是化学的一个关键概念，尽管人们付出了诸多努力，但从全粒子量子力学的角度来看，它仍然难以捉摸。我们将分子结构视为核位置之间强统计相关性的一种表现形式，提出了一种基于马尔可夫链蒙特卡罗采样和无监督机器学习的实用方法。将该方法应用于D分子明确表明它具有等边三角形结构。这些结果为我们从基本量子原理理解分子结构向前迈出了重要一步。
