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Improved Speed and Scaling in Orbital Space Variational Monte Carlo

机译:在轨道空间变差蒙特卡罗改进速度和缩放

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In this work, we introduce three algorithmic improvements to reduce the cost and improve the scaling of orbital space variational Monte Carlo (VMC). First, we show that, by appropriately screening the one- and two-electron integrals of the Hamiltonian, one can improve the efficiency of the algorithm by several orders of magnitude. This improved efficiency comes with the added benefit that the cost of obtaining a constant error per electron scales as the second power of the system size O(N-2), down from the fourth power O(N-4). Using numerical results, we demonstrate that the practical scaling obtained is, in fact, O(N-1.5) for a chain of hydrogen atoms. Second, we show that, by using the adaptive stochastic gradient descent algorithm called AMSGrad, one can optimize the wave function energies robustly and efficiently. Remarkably, AMSGrad is almost as inexpensive as the simple stochastic gradient descent but delivers a convergence rate that is comparable to that of the Stochastic Reconfiguration algorithm, which is significantly more expensive and has a worse scaling with the system size. Third, we introduce the use of the rejection-free continuous time Monte Carlo (CTMC) to sample the determinants. Unlike the first two improvements, CTMC does come at an overhead that the local energy must be calculated at every Monte Carlo step. However, this overhead is mitigated to a large extent because of the reduced scaling algorithm, which ensures that the asymptotic cost of calculating the local energy is equal to that of updating the walker. The resulting algorithm allows us to calculate the ground state energy of a chain of 160 hydrogen atoms using a wave function containing similar to 2 x 10(5) variational parameters with an accuracy of 1 mE(h)/particle at a cost of just 25 CPU h, which when split over 2 nodes of 24 processors each amounts to only about half hour of wall time. This low cost coupled with embarrassing parallelizability of the VMC algorithm and great freedom in the forms of usable wave functions, represents a highly effective method for calculating the electronic structure of model and ab initio systems.
机译:在这项工作中,我们介绍了三种算法改进以降低成本并改善轨道空间变分蒙特卡罗(VMC)的缩放。首先,我们表明,通过适当地筛选Hamiltonian的单电子积分,可以通过几个数量级来提高算法的效率。这种提高的效率随着从第四功率O(N-4)的系统尺寸O(n-2)的第二功率获得恒定误差的增加的效率,从而从第四功率O(n-4)下降。使用数值结果,我们证明所获得的实际缩放实际上是氢原子链的O(n-1.5)。其次,我们表明,通过使用称为AMSGRAD的自适应随机梯度下降算法,可以鲁棒地和有效地优化波函数能量。值得注意的是,AMSGRAD几乎与简单的随机梯度下降一样廉价,但提供与随机重新配置算法的收敛速率相当,这显着更昂贵并且具有更差的缩放与系统尺寸更令人昂贵。第三,我们介绍了无拒绝连续时间蒙特卡罗(CTMC)的使用来样的测定法。与前两种改进不同,CTMC确实陷入开销,即必须在每个蒙特卡罗步骤计算本地能量。然而,由于降低的缩放算法,这种开销在很大程度上减轻了,这确保了计算局部能量的渐近成本等于更新步行者的渐近成本。所得到的算法允许我们使用含有类似于2×10(5)个变分参数的波函数来计算160氢原子链的接地状态能量,其精度为1 me(h)/粒子,其成本仅为25 CPU H,当分割24个处理器的2个节点时,每个墙壁时间仅为半小时。这种低成本与VMC算法的令人尴尬的并行性相结合,并且以可用波函数的形式具有很大的自由度,表示用于计算模型和AB Initio系统的电子结构的高效方法。

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