Pushing the frontiers of density functionals by solving the fractional electron problem
Pushing the frontiers of density functionals by solving the fractional electron problem
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DOI：
10.1126/science.abj6511
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发表时间：
2021-12-10
期刊：
影响因子：
56.9
通讯作者：
Cohen, Aron J.
中科院分区：
文献类型：
--
作者：
Kirkpatrick, James;McMorrow, Brendan;Cohen, Aron J.
密度泛函理论在量子水平上描述物质，但所有流行的近似都存在系统误差，这些误差来自于违反精确泛函的数学性质。我们克服了这个基本的限制，训练神经网络的分子数据和虚构的系统与分数电荷和自旋。由此产生的泛函DM21（DeepMind 21）正确地描述了人工电荷离域和强相关性的典型示例，并且在主族原子和分子的全面基准上比传统泛函表现得更好。DM21精确地模拟了复杂的系统，如氢链、带电DNA碱基对和双自由基过渡态。对该领域来说更重要的是，因为我们的方法依赖于不断改进的数据和约束，它代表了一条通往精确通用功能的可行途径。
Density functional theory describes matter at the quantum level, but all popular approximations suffer from systematic errors that arise from the violation of mathematical properties of the exact functional. We overcame this fundamental limitation by training a neural network on molecular data and on fictitious systems with fractional charge and spin. The resulting functional, DM21 (DeepMind 21), correctly describes typical examples of artificial charge delocalization and strong correlation and performs better than traditional functionals on thorough benchmarks for main-group atoms and molecules. DM21 accurately models complex systems such as hydrogen chains, charged DNA base pairs, and diradical transition states. More crucially for the field, because our methodology relies on data and constraints, which are continually improving, it represents a viable pathway toward the exact universal functional.