论文标题
机器学习1-和2电子降低了聚合物分子的密度矩阵
Machine Learning 1- and 2-electron reduced density matrices of polymeric molecules
论文作者
论文摘要
使用2电子降低密度矩阵（2RDM）而不是多体波函数编码分子的电子结构已有数十年的追求，因为2RDM包含足够的信息来计算精确的分子能量，但只需要多项式存储。我们专注于具有不同构象和单体数量的线性聚合物，并表明我们可以使用机器学习来预测1-电子和2电子降低的密度矩阵。此外，通过将哈密顿操作员应用于预测的降低密度矩阵，我们表明我们可以恢复分子能。因此，我们证明了机器学习方法可以预测可推广到新构象和新分子的电子结构的可行性。同时，我们的工作规避了通过直接机器学习有效的有效降低密度矩阵来阻碍2RDM方法适应的N-陈述性问题。
Encoding the electronic structure of molecules using 2-electron reduced density matrices (2RDMs) as opposed to many-body wave functions has been a decades-long quest as the 2RDM contains sufficient information to compute the exact molecular energy but requires only polynomial storage. We focus on linear polymers with varying conformations and numbers of monomers and show that we can use machine learning to predict both the 1-electron and the 2-electron reduced density matrices. Moreover, by applying the Hamiltonian operator to the predicted reduced density matrices we show that we can recover the molecular energy. Thus, we demonstrate the feasibility of a machine learning approach to predicting electronic structure that is generalizable both to new conformations as well as new molecules. At the same time our work circumvents the N-representability problem that has stymied the adaption of 2RDM methods, by directly machine-learning valid Reduced Density Matrices.
