论文标题
通过Slater指数Ansatz提高费米子神经网络的性能
Improving the performance of fermionic neural networks with the Slater exponential Ansatz
论文作者
论文摘要
在这项工作中，我们提出了一种用于使用费米子神经网络（费摩尼特）与Slater指数ANSATZ用于电子 - 核和电子电子距离的技术，该技术由于更好地描述了与凝胶点的小囊泡的植物粒子相互作用，因此可以更快地收敛目标地基状态能量。学习曲线的分析表明，使用包装方法的参数获得具有较小批量的精确能量的可能性。为了获得地面能量的更准确的结果，我们建议采用推断方案，该方案估计蒙特卡洛积分以无限数量的限制。一组分子的数值测试证明了与原始费米特的结果（具有比我们的方法所要求的更大的批量大小实现），以及耦合群集单打的结果，并使用扰动三元三倍（CCSD（T））进行了完整基集（CBS）限制（CBS）限制。
In this work, we propose a technique for the use of fermionic neural networks (FermiNets) with the Slater exponential Ansatz for electron-nuclear and electron-electron distances, which provides faster convergence of target ground-state energies due to a better description of the interparticle interaction in the vicinities of the coalescence points. Analysis of learning curves indicates on the possibility to obtain accurate energies with smaller batch sizes using arguments of the bagging approach. In order to obtain even more accurate results for the ground-state energies, we suggest an extrapolation scheme, which estimates Monte Carlo integrals in the limit of an infinite number of points. Numerical tests for a set of molecules demonstrate a good agreement with the results of original FermiNets (achieved with larger batch sizes than required by our approach) and with results of coupled-cluster singles and doubles with perturbative triples (CCSD(T)) method, calculated in the complete basis set (CBS) limit.
