Data-Driven Refinement of Electronic Energies from Two-Electron Reduced-Density-Matrix Theory
Data-Driven Refinement of Electronic Energies from Two-Electron Reduced-Density-Matrix Theory
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从双电子约化密度矩阵理论对电子能量进行数据驱动的细化
DOI：
10.1021/acs.jpclett.3c01382
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Vogiatzis, Konstantinos D.
中科院分区：
文献类型：
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作者：
Jones, Grier M.;Li, Run R.;DePrince, A. Eugene;Vogiatzis, Konstantinos D.
通过采用基于降密度矩阵（RDM）的电子结构描述，可以降低描述强相关电子的指数计算成本。虽然变分双电子RDM （v2RDM）方法可以在这样的系统上进行大规模计算，但解决方案的质量受到这样一个事实的限制，即在实际计算中，只有已知必要的n -可表示性约束的子集可以应用于2RDM。在这里，我们证明了部分三粒子（T1和T2） n -可表征性条件的违反，可以仅用2RDM的知识来评估，可以作为机器学习（ML）协议中的基于物理的特征，用于提高仅考虑两粒子（PQG）条件的v2RDM计算的能量。原理证明计算表明，相对于基于构型相互作用的计算的参考值，该模型产生的能量大大提高。
The exponential computational cost of describing strongly correlated electrons can be mitigated by adopting a reduced-density matrix (RDM)-based description of the electronic structure. While variational two-electron RDM (v2RDM) methods can enable large-scale calculations on such systems, the quality of the solution is limited by the fact that only a subset of known necessaryN-representability constraints can be applied to the 2RDM in practical calculations. Here, we demonstrate that violations of partial three-particle (T1 and T2)N-representability conditions, which can be evaluated with knowledge of only the 2RDM, can serve asphysics-basedfeatures in a machine-learning (ML) protocol for improving energies from v2RDM calculations that consider only two-particle (PQG) conditions. Proof-of-principle calculations demonstrate that the model yields substantially improved energies relative to reference values from configuration-interaction-based calculations.