Otis Leon, Craig Isabel, Neuscamman Eric
Department of Physics, University of California, Berkeley, Berkeley, California 94720, USA.
Department of Chemistry, University of California, Berkeley, Berkeley, California 94720, USA.
J Chem Phys. 2020 Dec 21;153(23):234105. doi: 10.1063/5.0024572.
We extend our hybrid linear-method/accelerated-descent variational Monte Carlo optimization approach to excited states and investigate its efficacy in double excitations. In addition to showing a superior statistical efficiency when compared to the linear method, our tests on small molecules show good energetic agreement with benchmark methods. We also demonstrate the ability to treat double excitations in systems that are too large for a full treatment by using selected configuration interaction methods via an application to 4-aminobenzonitrile. Finally, we investigate the stability of state-specific variance optimization against collapse to other states' variance minima and find that symmetry, Ansatz quality, and sample size all have roles to play in achieving stability.
我们将混合线性方法/加速下降变分蒙特卡罗优化方法扩展到激发态，并研究其在双激发中的有效性。除了与线性方法相比显示出更高的统计效率外，我们对小分子的测试表明，与基准方法在能量上有良好的一致性。我们还通过对4-氨基苯腈的应用，展示了使用选定的组态相互作用方法处理对于完全处理来说太大的系统中的双激发的能力。最后，我们研究了特定状态方差优化相对于坍缩到其他状态方差最小值的稳定性，发现对称性、近似质量和样本大小在实现稳定性方面都发挥着作用。
